Using randomness compensating factors to improve forecast accuracy

ABSTRACT

Input data that includes one or more observations made at one or more corresponding time slices is received. The input data is provided to one or more time-based prediction models that predict one or more values for one or more future time slices. One or more randomness compensating factors are determined. The one or more randomness compensating factors correspond to one or more features indicative of one or more events associated with the one or more corresponding time slices. Based at least in part on the determining of the one or more randomness compensating factors and the prediction of the one or more time-based prediction models, a second one or more values for at least one of the one or more future time slices are predicted. In response to the predicting of the second one or more values, an indication associated with the predicted second one or more values are presented to a user computer device.

BACKGROUND

Computer-implemented technologies can assist users by makingpredictions, and in particular, by making time-based predictions. Forexample, some computer applications are configured to use machinelearning models or forecast models to make time sequence forecasts. Timeseries forecast models, for instance, correspond to algorithms that areused to predict future values at certain times based on previouslyobserved values at certain times. Despite these advances, these modelsand computer applications suffer from a number of disadvantages,particularly in terms of their accuracy. This is due at least in partbecause the predictions of these applications fail to utilize hidden orlatent factors that are important for making time-based predictions. Tothese applications and models, these latent factors are simplyindicative of randomness, noise, or outliers, when in fact they are not.

SUMMARY

Various embodiments of the present disclosure are directed to a system,a computer-implemented method, and a computer readable storage medium.In some aspects, the system includes at least one computing devicehaving at least one processor and at least one computer readable storagemedium having program instructions embodied therewith. The programinstructions are readable or executable by the at least one processor tocause the system to perform the following operations in some aspects.Input data that includes a plurality of observations made atcorresponding time slices are received. The input data is provided toone or more base time-series forecasting models that predict one or morefuture values for one or more future time slices based on the one ormore time-series forecasting models analyzing the input data. One ormore randomness compensating factors are determined. The one or morerandomness compensating factors correspond to one or more featuresindicative of one or more events that occur on one or more of thecorresponding time slices. The one or more features are not indicated inthe one or more base time-series forecasting models. For one or more ofthe corresponding time slices, one or more patterns of observationsassociated with the one or more randomness compensating factors aredetermined. Based at least in part on the one or more randomnesscompensating factors and the one or more patterns, the prediction of theone or more base time-series forecasting models is modified. In responseto the modifying of the prediction, an indication associated with themodified prediction is presented to a user computer device.

In some aspects, the computer-implemented method includes the followingoperations. Input data that includes a plurality of observations made atcorresponding time slices is received. The input data is provided to oneor more time-based prediction models that predict one or more futurevalues for one or more future time slices. One or more randomnesscompensating factors are determined. The one or more randomnesscompensating factors correspond to one or more features indicative ofone or more events that occur on one or more of the corresponding timeslices. For one or more of the corresponding time slices, one or morepatterns of observations associated with the one or more randomnesscompensating factors are determined. Based at least in part on the oneor more randomness compensating factors and the one or more patterns,the prediction of the one or more time-based prediction models ismodified. In response to the modifying of the prediction, an indicationassociated with the modified prediction is presented to a user computerdevice.

In some aspects, the computer readable storage medium has programinstructions embodied therewith. In some aspects, the programinstructions are executable by one or more processors to cause the oneor more processors to perform the following operations. Input data thatincludes one or more observations made at one or more corresponding timeslices is received. The input data is provided to one or more time-basedprediction models that predict one or more values for one or more futuretime slices. One or more randomness compensating factors are determined.The one or more randomness compensating factors correspond to one ormore features indicative of one or more events associated with the oneor more corresponding time slices. Based at least in part on thedetermining of the one or more randomness compensating factors and theprediction of the one or more time-based prediction models, a second oneor more values for at least one of the one or more future time slicesare predicted. In response to the predicting of the second one or morevalues, an indication associated with the predicted second one or morevalues are presented to a user computer device.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used in isolation as an aid in determining the scope of the claimedsubject matter. In various embodiments, any functionality can be addedor removed from the computer-implemented method, the system, and theapparatus described above, such as functionality described with respectto the flow diagrams.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 is a schematic diagram of an example computing environment inwhich aspects of the present disclosure are employed in, according tosome embodiments.

FIG. 2 is a schematic diagram of an analysis computing entity in whichaspects of the present disclosure are employed in, according to someembodiments.

FIG. 3 is a schematic diagram of a computing entity in which aspects ofthe present disclosure are employed in, according to some embodiments.

FIG. 4 is a block diagram of the logistics vehicle of FIG. 1, accordingto some embodiments.

FIG. 5 is a block diagram of a system for making a time-basedprediction, according to some embodiments.

FIG. 6 is a schematic diagram illustrating how a composite observationcan be broken down into a base model, randomness compensating factorpatterns, and residual randomness, according to some embodiments.

FIG. 7 is a time-series graph that specifically illustrates a base modelvolume observation relative to a volume observation associated with aparticular randomness compensating factor for the same time slices,according to some embodiments.

FIG. 8A is a schematic diagram of an example exponential smoothingforecast model table, according to some embodiments.

FIG. 8B is a schematic diagram of an example time series graphassociated with the table of FIG. 8A, according to some embodiments.

FIG. 8C is a schematic diagram of an example exponential smoothingforecast model table with adjusted values relative to FIG. 8A, accordingto some embodiments.

FIG. 8D is a schematic diagram of an example time series graphassociated with the table of FIG. 8C, according to some embodiments.

FIG. 9 is a schematic diagram of a mobile device indicating an alertthat is presented based on making a prediction, according to someembodiments.

FIG. 10 is a flow diagram of an example process for generating aprediction, according to some embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the disclosure are shown. Indeed, the disclosure may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. Rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

I. Overview

Some computer applications may use one or more models (e.g., exponentialsmoothing, hidden Markov models, etc.) to make time-based predictions.For example, previously observed values may indicate that at the end ofa first year there were 3000 sales and at the end of a second year,there were 3050 sales. A very simple forecast model might, for example,predict the number of sales that will occur at the end of a third yearby taking the average of these two values of the first and second year(3,025).

As described above, existing time-based prediction computer applicationsare often inaccurate. Various existing computer applications utilizeseveral time series forecasting models with seasonality (e.g., periodicfluctuations at certain time intervals), expect the models to learn thecontext, and pick an output from one of the models that looks the mostpromising. These solutions hope to achieve high levels of accuracywithout an intentional mechanism to improve the accuracy. However, thisone-size-fits-all approach is very static and does not account for veryspecific latent factors that indicate various patterns of observations.For example, if an application were to predict pickup volume (the amountof parcels that will be processed) at a specific logistics facility (adomain), among many logistics stores, the precise prediction associatedwith the specific logistics store can be influenced by many latentfactors, such as the location of the specific logistics store, the otherbusinesses in the locale of the specific logistics store, local andnational holidays, sale events at the logistics store only, hugebusiness recalls, weather around the specific logistics store,geo-political events in the area that the specific logistics store iswithin, and the like. However, other domains or facilities may not beaffected by the same factors to the same prediction magnitude as thisspecific logistics store, which is why these factors may be viewed aslatent or hidden.

When all of these factors are combined and an observation is made at thesame time with existing models, the outcome at an aggregate level mayappear random or noisy with no apparent pattern. Existing models, suchas time-series forecast models, fail to account for the noise altogetheror inadequately deal with the noise based on aggregating the seeminglyrandom behavior into a value that indicates a probable outcome based ona majority observation of other data points. These seemingly randomphenomena are typically tagged as outliers and do not carry predictiveweight.

As described above, this noise-like randomness is often made up ofseveral latent factors that account for specific patterns ofobservations over time. As the number of factors that affect theprediction are very independent for given domains, the accuracy of themodels are typically not improved beyond a certain point because thecumulative effect of several factors reduce the accuracy.

Various embodiments of the present disclosure improve these existingtechnologies by reducing the effect of apparent randomness or noise inforecast accuracy predictions. Although true randomness may not bemodeled, particular embodiments identify one or more random compensatingfactors (e.g., latent factors that appear to be random but are not),determine one or more patterns over time associated with the one or morerandom compensating factors (e.g., via additive and subtractive harmonicsynthesis), and/or generate a prediction using the one or more randomcompensating factors to improve accuracy.

Although some machine learning models can learn (e.g., via neural nodeconnection weighting during training) which features are more importantfor a given prediction, these technologies still use the same factors asinput. One assumption is that the specific universe of factors that havebeen identified are sufficient to make accurate predictions for everydomain. However, latent factors may never be identified and thereforeused for the machine learning training or learning, even though they maybe crucial for making predictions for certain domains. Moreover, even ifspecific factors have been learned to be associated with a particularprediction, various rounds of training may cause overfitting of themodel. When a model gets trained with a lot of data it often startslearning from inaccurate data entries in a data set. This causesinaccuracies based on the inaccurate data entries.

Various embodiments of the present disclosure improve existing machinelearning models because they do not use the same factors as input forevery domain (e.g., particular logistics facilities) and do not train oninaccurate data. This effectively reduces or removes randomness forparticular domains and thus effectively eliminates or reducesoverfitting. For instance, in predicting the amount of volume that willbe processed, a first sorting center may use a base model (e.g.,exponential smoothing model) and then a first randomness compensatingfactor (e.g., weather). A second sorting second may also use the basemodel but then use a second randomness compensating factor (e.g., ageopolitical event) instead of the first randomness compensating factor.There may be particular patterns of observations regarding weather forthe first sorting center (but not the second sorting center). Likewisethere may be particular patterns of observations regarding thegeopolitical event for the second sorting center (but not the firstsorting center). Accordingly, in each case randomness can be reduced forboth sorting centers and the same factors are not used for both sortingcenters.

Various embodiments of the present disclosure additionally improveconventional techniques used in the shipping industry for makinglogistics-based forecasts. For instance, conventional techniques forpredicting in the shipping industry include using generic spreadsheet orgraphical trend computer applications to predict sort volume (e.g., theamount of parcels that will get processed) in a sorting center, predictbuilding operational capacity (e.g., predicting staff capacity), predictthe length of time a delivery will take, and the like. However, usingthe techniques described above, such as using one or more randomcompensating factors, embodiments can make these predictions moreaccurate, which can greatly impact how efficient logistics functionsoperate.

It is understood that although this overview section describes variousimprovements to conventional solutions and technologies, these are byway of example only. As such, other improvements are described below orwill become evident through description of various embodiments. Thisoverview is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This overview is not intended to: identify key features oressential features of the claimed subject matter, key improvements, noris it intended to be used in isolation as an aid in determining thescope of the claimed subject matter.

II. Apparatuses, Methods, and Systems

Embodiments of the present disclosure may be implemented in variousways, including as apparatuses that comprise articles of manufacture. Anapparatus may include a non-transitory computer-readable storage mediumstoring applications, programs, program modules, scripts, source code,program code, object code, byte code, compiled code, interpreted code,machine code, executable instructions, and/or the like (also referred toherein as executable instructions, instructions for execution, programcode, and/or similar terms used herein interchangeably). Suchnon-transitory computer-readable storage media include allcomputer-readable media (including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM)), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), doubleinformation/data rate synchronous dynamic random access memory (DDRSDRAM), double information/data rate type two synchronous dynamic randomaccess memory (DDR2 SDRAM), double information/data rate type threesynchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamicrandom access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM(T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM),dual in-line memory module (DIMM), single in-line memory module (SIMM),video random access memory (VRAM), cache memory (including variouslevels), flash memory, register memory, and/or the like. It will beappreciated that where embodiments are described to use acomputer-readable storage medium, other types of computer-readablestorage media may be substituted for or used in addition to thecomputer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatus, systems, computingdevices/entities, computing entities, and/or the like. As such,embodiments of the present disclosure may take the form of an apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. However, embodiments of the presentdisclosure may also take the form of an entirely hardware embodimentperforming certain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computingdevices/entities, computing entities, and/or the like carrying outinstructions, operations, steps, and similar words used interchangeably(e.g., the executable instructions, instructions for execution, programcode, and/or the like) on a computer-readable storage medium forexecution. For example, retrieval, loading, and execution of code may beperformed sequentially such that one instruction is retrieved, loaded,and executed at a time. In some exemplary embodiments, retrieval,loading, and/or execution may be performed in parallel such thatmultiple instructions are retrieved, loaded, and/or executed together.Thus, such embodiments can produce specifically-configured machinesperforming the steps or operations specified in the block diagrams andflowchart illustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

III. Exemplary System Architecture

FIG. 1 is a schematic diagram of an example computing environment 100 inwhich aspects of the present disclosure are employed in, according tosome embodiments. As shown in FIG. 1, this particular computingenvironment 100 includes one or more logistics vehicles 120, one or moreanalysis computing entities 105, one or more computing entities 110(e.g., a mobile device, such as a DIAD), one or more satellites 112, oneor more networks 135, a data corpus 160 and/or the like. Each of thesecomponents, entities, devices, systems, and similar words used hereininterchangeably may be in direct or indirect communication with, forexample, one another over the same or different wired and/or wirelessnetworks. Additionally, while FIG. 1 illustrates the various systementities as separate, standalone entities, the various embodiments arenot limited to this particular architecture.

In various embodiments, the network(s) 135 represents or includes an IoTor IoE network, which is a network of interconnected items that are eachprovided with unique identifiers (e.g., UIDs) and computing logic so asto communicate or transfer data with each other or other components.Such communication can happen without requiring human-to-human orhuman-to-computer interaction. For example, a IoT network may includethe logistics vehicle 120, which is equipped with one or more sensorsand transmitter in order to process and/or transmit sensor data over thenetwork 135 to the analysis computing entity(s) 105. In the context ofan IoT network, a computer (not shown) within the logistics vehicle 120can be or include one or more local processing devices (e.g., edgenodes) that are one or more computing devices configured to store andprocess, over the network(s) 135, either a subset or all of the receivedor respective sets of data to the one or more remote computing devices(e.g., the computing entities 110 and/or the analysis computingentity(s) 105) for analysis.

In some embodiments, the local processing device(s) is a mesh or othernetwork of microdata centers or edge nodes that process and store localdata received from sensors coupled to the logistics vehicle 120 and pushor transmit some or all of the data to a cloud device or a corporatedata center that is or is included in the one or more analysis computingentities 105. In some embodiments, the local processing device(s) storeall of the data and only transmit selected (e.g., data that meets athreshold) or important data to the one or more analysis computingentities 105. Accordingly, the non-important data or the data that is ina group that does not meet a threshold is not transmitted. For example,a lidar, radar, and/or camera sensor located within the logisticsvehicle 120 may sample map data but only push a portion of the map data.Accordingly, only after the condition or threshold has been met, do thelocal processing device(s) transmit the data that meets or exceeds thethreshold to remote computing devices such that the remote device(s) cantake responsive actions, such as notify a user mobile device (e.g.,computing entity 110) indicating the threshold has been met and/or causea modification of a device to perform an action (e.g., turn based on thecontrol signal received). The data that does not meet or exceed thethreshold is not transmitted in particular embodiments. In variousembodiments where the threshold or condition is not met, daily or othertime period reports are periodically generated and transmitted from thelocal processing device(s) to the remote device(s) indicating all thedata readings gathered and processed at the local processing device(s).In some embodiments, the one or more local processing devices act as abuffer or gateway between the network(s) and a broader network, such asthe one or more networks 135. Accordingly, in these embodiments, the oneor more local processing devices can be associated with one or moregateway devices that translate proprietary communication protocols intoother protocols, such as internet protocols.

The data corpus 160 represents a data store (e.g. a database) or set ofdata stores (e.g., a distributed set of storage devices) that storesdata concerning specific domains. In this way, randomness compensatingfactors can be determined and used for making a time-based prediction.For example, the data corpus 160 can store information concerning localand national holidays associated with a sorting center, weatherassociated with the sorting center, geo-political events in an areawhere the sorting center is located, the businesses in the locale in thesorting center, and the like. In this way, the analysis computing entity105 can determine one or more randomness compensating factors anddetermine one or more observational patterns over time in connectionwith the randomness compensating factors. For example, the data corpus160 can store information indicating that a first logistics store is inan area where there is a local holiday every particular day every year.In particular embodiments, the analysis computing entity 105 can obtainthis information (e.g., via the network 135) and determine that this isa randomness compensating factor, make a time-based prediction forecast,identify one or more patterns associated with the randomnesscompensating factor, modify the forecast with the randomnesscompensating factor, and then responsively send a notification to thecomputing entity 110 and/or send a control signal to an apparatus (e.g.,the logistics vehicle 120, a conveyor belt (not shown)), which isdescribed in more detail below. In some embodiments, the one or moreanalysis computing entities 105 obtain the data for determiningrandomness compensating factors from the logistics vehicle 120 inaddition or alternative to the data corpus 160.

1. Exemplary Analysis Computing Entities

FIG. 2 provides a schematic of an analysis computing entity 105according to particular embodiments of the present disclosure. Ingeneral, the terms computing entity, computer, entity, device, system,and/or similar words used herein interchangeably may refer to, forexample, one or more computers, computing entities, desktops, mobilephones, tablets, phablets, notebooks, laptops, distributed systems,consoles input terminals, servers or server networks, blades, gateways,switches, processing devices, processing entities, set-top boxes,relays, routers, network access points, base stations, the like, and/orany combination of devices or entities adapted to perform the functions,operations, and/or processes described herein. Such functions,operations, and/or processes may include, for example, transmitting,receiving, operating on, processing, displaying, storing, determining,creating/generating, monitoring, evaluating, comparing, and/or similarterms used herein interchangeably. In particular embodiments, thesefunctions, operations, and/or processes can be performed on data,content, information/data, and/or similar terms used hereininterchangeably.

As indicated, in particular embodiments, the analysis computing entity105 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatingdata, content, information/data, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like.

As shown in FIG. 2, in particular embodiments, the analysis computingentity 105 may include or be in communication with one or moreprocessing elements 205 (also referred to as processors, processingcircuitry, and/or similar terms used herein interchangeably) thatcommunicate with other elements within the analysis computing entity 105via a bus, for example. As will be understood, the processing element205 may be embodied in a number of different ways. For example, theprocessing element 205 may be embodied as one or more complexprogrammable logic devices (CPLDs), microprocessors, multi-coreprocessors, co-processing entities, application-specific instruction-setprocessors (ASIPs), microcontrollers, and/or controllers. Further, theprocessing element 205 may be embodied as one or more other processingdevices or circuitry. The term circuitry may refer to an entirelyhardware embodiment or a combination of hardware and computer programproducts. Thus, the processing element 205 may be embodied as integratedcircuits, application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), programmable logic arrays (PLAs),hardware accelerators, other circuitry, and/or the like. As willtherefore be understood, the processing element 205 may be configuredfor a particular use or configured to execute instructions stored involatile or non-volatile media or otherwise accessible to the processingelement 205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present disclosure when configured accordingly.

In particular embodiments, the analysis computing entity 105 may furtherinclude or be in communication with non-volatile media (also referred toas non-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the non-volatile storage or memory may include one or more non-volatilestorage or memory media 210, including but not limited to hard disks,ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipedememory, racetrack memory, and/or the like. As will be recognized, thenon-volatile storage or memory media may store databases (e.g.,parcel/item/shipment database), database instances, database managementsystems, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like. The term database,database instance, database management system, and/or similar terms usedherein interchangeably may refer to a collection of records orinformation/data that is stored in a computer-readable storage mediumusing one or more database models, such as a hierarchical databasemodel, network model, relational model, entity-relationship model,object model, document model, semantic model, graph model, and/or thelike.

In particular embodiments, the analysis computing entity 105 may furtherinclude or be in communication with volatile media (also referred to asvolatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In particular embodiments,the volatile storage or memory may also include one or more volatilestorage or memory media 215, including but not limited to RAM, DRAM,SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM,RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. As will be recognized, the volatilestorage or memory media may be used to store at least portions of thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like being executed by, for example,the processing element 205. Thus, the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likemay be used to control certain aspects of the operation of the analysiscomputing entity 105 with the assistance of the processing element 205and operating system.

As indicated, in particular embodiments, the analysis computing entity105 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatinginformation/data, content, information/data, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired information/data transmission protocol, such asfiber distributed information/data interface (FDDI), digital subscriberline (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay,information/data over cable service interface specification (DOCSIS), orany other wired transmission protocol. Similarly, the analysis computingentity 105 may be configured to communicate via wireless externalcommunication networks using any of a variety of protocols, such asgeneral packet radio service (GPRS), Universal Mobile TelecommunicationsSystem (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA20001× (1×RTT), Wideband Code Division Multiple Access (WCDMA), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, long range lowpower (LoRa), LTE Cat M1, NarrowBand IoT (NB IoT), and/or any otherwireless protocol.

Although not shown, the analysis computing entity 105 may include or bein communication with one or more input elements, such as a keyboardinput, a mouse input, a touch screen/display input, motion input,movement input, audio input, pointing device input, joystick input,keypad input, and/or the like. The analysis computing entity 105 mayalso include or be in communication with one or more output elements(not shown), such as audio output, video output, screen/display output,motion output, movement output, and/or the like.

As will be appreciated, one or more of the analysis computing entity's105 components may be located remotely from other analysis computingentity 105 components, such as in a distributed system. Additionally oralternatively, the analysis computing entity 105 may be representedamong a plurality of analysis computing entities. For example, theanalysis computing entity 105 can be or be included in a cloud computingenvironment, which includes a network-based, distributed/data processingsystem that provides one or more cloud computing services. Further, acloud computing environment can include many computers, hundreds orthousands of them or more, disposed within one or more data centers andconfigured to share resources over the network(s) 135. Furthermore, oneor more of the components may be combined and additional componentsperforming functions described herein may be included in the analysiscomputing entity 105. Thus, the analysis computing entity 105 can beadapted to accommodate a variety of needs and circumstances. As will berecognized, these architectures and descriptions are provided forexemplary purposes only and are not limiting to the various embodiments.

2. Exemplary Computing Entities

Computing entities 110 may be configured for registering one or moreusers, processing one or more shipping requests, securing parcels,monitoring shipments, and/or for operation by a user (e.g., a vehicleoperator, delivery personnel, customer, and/or the like). In certainembodiments, computing entities 110 may be embodied as handheldcomputing entities, such as mobile phones, tablets, personal digitalassistants, and/or the like, that may be operated at least in part basedon user input received from a user via an input mechanism. Moreover,computing entities 110 may be embodied as onboard vehicle computingentities, such as central vehicle electronic control units (ECUs),onboard multimedia system, and/or the like that may be operated at leastin part based on user input. Such onboard vehicle computing entities maybe configured for autonomous and/or nearly autonomous operation however,as they may be embodied as onboard control systems for autonomous orsemi-autonomous vehicles, such as unmanned aerial vehicles (UAVs),robots, and/or the like. As a specific example, computing entities 110may be utilized as onboard controllers for UAVs configured forpicking-up and/or delivering packages to various locations, andaccordingly such computing entities 110 may be configured to monitorvarious inputs (e.g., from various sensors) and generated variousoutputs. It should be understood that various embodiments of the presentdisclosure may comprise a plurality of computing entities 110 embodiedin one or more forms (e.g., parcel security devices kiosks, mobiledevices, watches, smart glasses, laptops, carrier personnel devices(e.g., Delivery Information Acquisition Devices (DIAD)), etc.)

As will be recognized, a user may be an individual, a family, a company,an organization, an entity, a department within an organization, arepresentative of an organization and/or person, and/or the like—whetheror not associated with a carrier. In particular embodiments, a user mayoperate a computing entity 110 that may include one or more componentsthat are functionally similar to those of the analysis computing entity105. FIG. 3 provides an illustrative schematic representative of acomputing entity 110 that can be used in conjunction with embodiments ofthe present disclosure. In general, the terms device, system, computingentity, entity, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, vehicle multimedia systems, autonomous vehicleonboard control systems, watches, glasses, key fobs, radio frequencyidentification (RFID) tags, ear pieces, scanners, imagingdevices/cameras (e.g., part of a multi-view image capture system),wristbands, kiosks, input terminals, servers or server networks, blades,gateways, switches, processing devices, processing entities, set-topboxes, relays, routers, network access points, base stations, the like,and/or any combination of devices or entities adapted to perform thefunctions, operations, and/or processes described herein. Computingentities 110 can be operated by various parties, including carrierpersonnel (sorters, loaders, delivery drivers, network administrators,and/or the like). As shown in FIG. 3, the computing entity 110 caninclude an antenna 312, a transmitter 304 (e.g., radio), a receiver 306(e.g., radio), and a processing element 308 (e.g., CPLDs,microprocessors, multi-core processors, coprocessing entities, ASIPs,microcontrollers, and/or controllers) that provides signals to andreceives signals from the transmitter 304 and receiver 306,respectively. In some embodiments, the computing entity 110 includes oneor more sensors 330. In this way, the computing entity 110 is aspecial-purpose computer or particular machine. In some embodiments, atleast one of the computing entities 110 is coupled to the logisticsvehicle 120 (e.g., within the trunk). The one or more sensors 330 can beone or more of: a pressure sensor, an accelerometer, a gyroscope, ageolocation sensor (e.g., GPS sensor), a radar, a lidar, sonar,ultrasound, an object recognition camera, and any other suitable sensorused to detect objects in a geographical environment.

The signals provided to and received from the transmitter 304 and thereceiver 306, respectively, may include signaling information inaccordance with air interface standards of applicable wireless systems.In this regard, the computing entity 110 may be capable of operatingwith one or more air interface standards, communication protocols,modulation types, and access types. More particularly, the computingentity 110 may operate in accordance with any of a number of wirelesscommunication standards and protocols, such as those described abovewith regard to the analysis computing entity 105. In a particularembodiment, the computing entity 110 may operate in accordance withmultiple wireless communication standards and protocols, such as UMTS,CDMA2000, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA,Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or thelike. Similarly, the computing entity 110 may operate in accordance withmultiple wired communication standards and protocols, such as thosedescribed above with regard to the analysis computing entity 105 via anetwork interface 320.

Via these communication standards and protocols, the computing entity110 can communicate with various other entities using concepts such asUnstructured Supplementary Service information/data (USSD), ShortMessage Service (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The computing entity 110 can also download changes,add-ons, and updates, for instance, to its firmware, software (e.g.,including executable instructions, applications, program modules), andoperating system.

According to particular embodiments, the computing entity 110 mayinclude location determining aspects, devices, modules, functionalities,and/or similar words used herein interchangeably. For example, thecomputing entity 110 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In particularembodiments, the location module can acquire information/data, sometimesknown as ephemeris information/data, by identifying the number ofsatellites in view and the relative positions of those satellites (e.g.,using global positioning systems (GPS)). The satellites may be a varietyof different satellites, including Low Earth Orbit (LEO) satellitesystems, Department of Defense (DOD) satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. This information/data can be collected using a variety ofcoordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes,Seconds (DMS); Universal Transverse Mercator (UTM); Universal PolarStereographic (UPS) coordinate systems; and/or the like. Alternatively,the location information can be determined by triangulating thecomputing entity's 110 position in connection with a variety of othersystems, including cellular towers, Wi-Fi access points, and/or thelike. Similarly, the computing entity 110 may include indoor positioningaspects, such as a location module adapted to acquire, for example,latitude, longitude, altitude, geocode, course, direction, heading,speed, time, date, and/or various other information/data. Some of theindoor systems may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices/entities (e.g.,smartphones, laptops) and/or the like. For instance, such technologiesmay include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy(BLE) transmitters, NFC transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The computing entity 110 may also comprise a user interface (that caninclude a display 316 coupled to a processing element 308) and/or a userinput interface (coupled to a processing element 308). For example, theuser interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the computing entity 110 to interact with and/or causedisplay of information from the analysis computing entity 105, asdescribed herein. The user input interface can comprise any of a numberof devices or interfaces allowing the computing entity 110 to receiveinformation/data, such as a keypad 318 (hard or soft), a touch display,voice/speech or motion interfaces, or other input device. In embodimentsincluding a keypad 318, the keypad 318 can include (or cause display of)the conventional numeric (0-9) and related keys (#, *), and other keysused for operating the computing entity 110 and may include a full setof alphabetic keys or set of keys that may be activated to provide afull set of alphanumeric keys. In addition to providing input, the userinput interface can be used, for example, to activate or deactivatecertain functions, such as screen savers and/or sleep modes.

As shown in FIG. 3, the computing entity 110 may also include an camera,imaging device, and/or similar words used herein interchangeably 326(e.g., still-image camera, video camera, IoT enabled camera, IoT modulewith a low resolution camera, a wireless enabled MCU, and/or the like)configured to capture images. The computing entity 110 may be configuredto capture images via the onboard camera 326, and to store those imagingdevices/cameras locally, such as in the volatile memory 322 and/ornon-volatile memory 324. As discussed herein, the computing entity 110may be further configured to match the captured image data with relevantlocation and/or time information captured via the location determiningaspects to provide contextual information/data, such as a time-stamp,date-stamp, location-stamp, and/or the like to the image data reflectiveof the time, date, and/or location at which the image data was capturedvia the camera 326. The contextual data may be stored as a portion ofthe image (such that a visual representation of the image data includesthe contextual data) and/or may be stored as metadata (e.g., data thatdescribes other data, such as describing a payload) associated with theimage data that may be accessible to various computing entities 110.

The computing entity 110 may include other input mechanisms, such asscanners (e.g., barcode scanners), microphones, accelerometers, RFIDreaders, and/or the like configured to capture and store variousinformation types for the computing entity 110. For example, a scannermay be used to capture parcel/item/shipment information/data from anitem indicator disposed on a surface of a shipment or other item. Incertain embodiments, the computing entity 110 may be configured toassociate any captured input information/data, for example, via theonboard processing element 308. For example, scan data captured via ascanner may be associated with image data captured via the camera 326such that the scan data is provided as contextual data associated withthe image data.

The computing entity 110 can also include volatile storage or memory 322and/or non-volatile storage or memory 324, which can be embedded and/ormay be removable. For example, the non-volatile memory may be ROM, PROM,EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory,racetrack memory, and/or the like. The volatile memory may be RAM, DRAM,SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM,RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. The volatile and non-volatile storageor memory can store databases, database instances, database managementsystems, information/data, applications, programs, program modules,scripts, source code, object code, byte code, compiled code, interpretedcode, machine code, executable instructions, and/or the like toimplement the functions of the computing entity 110. As indicated, thismay include a user application that is resident on the entity oraccessible through a browser or other user interface for communicatingwith the analysis computing entity 105 and/or various other computingentities.

In another embodiment, the computing entity 110 may include one or morecomponents or functionality that are the same or similar to those of theanalysis computing entity 105, as described in greater detail above. Aswill be recognized, these architectures and descriptions are providedfor exemplary purposes only and are not limiting to the variousembodiments.

3. Exemplary Logistics Vehicle

FIG. 4 is a block diagram of the logistics vehicle 120 of FIG. 1,according to some embodiments. Although the logistics vehicle 120 isrepresented as a specific vehicle with specific sensors, it isunderstood that any suitable vehicle and/or sensor may exist. Forexample, in some embodiments, the logistics vehicle 120 is representedas a drone or unmanned aerial vehicle (UAV) that travels in air space tounload parcels, an aircraft, a car, a boat, etc. A “logistics vehicle”as described herein is any transportation vehicle that is configured toperform one or more shipping operations. For example, a logisticsvehicle can be an air-traversing drone, a tractor trailer, a van,delivery shuttle, shifter, line-hauler, and/or an airplane that carriesone or more parcels. A “shipping operation” is any activity (eithercompleted or initially engaged in) related to the shipment of one ormore parcels. For example, a shipping operation can include thebeginning or completion of a delivery and/or pick up one or more parcels(e.g., packages, envelopes, bags, pallets, etc.) to and/or from adestination or pickup point (e.g., a delivery home or business address,shipping locker, geocoordinates (e.g., a specific part of a property) ofan area by an address, etc.), also known as final mile delivery.

In these embodiments, the traversal of logistics vehicles through ageographical environment typically occurs via one or more deliveryroutes such that the logistics vehicle traverses multiple differentdestination or pickup points along the delivery routes. In theseembodiments, traversal of logistics vehicles along a route is typicallyproceeded by a request from a shipper to ship one or more items. Forexample, a shipper can arrive at a logistics store where the shipperpays for the shipping of a parcel, after which the parcel is loaded ontothe logistics vehicle to a sorting center or its destination. Any one ofthese steps can be a “shipping operation”. In another example, a usermay be presented with a user interface of a web page or app page that isconnected to a logistics network or other third party (e.g., ane-commerce merchant) and where the user can electronically issue arequest to ship one or more items. Subsequently, the item can be loadedinto the logistics vehicle. Any one of these steps can be a “shippingoperation”. A shipping operation can alternatively or additionallyinclude delivering packages from a logistics store, locker, or addressin a tractor trailer (or other vehicle) to a shipping facility, such asa sorting center. A sorting center is a facility where parcels areculled, labeled, and otherwise organized in preparation for final miledelivery. In some embodiments, a shipping operation can also includepre-activity or post-activity of final mile delivery or other shippingoperations. For example, a shipping operation can include the time afterwhich one or more logistics vehicles finish a delivery route ordelivering parcels and are one their way back to a facility (e.g., asorting facility).

In various embodiments, the logistics vehicle 120 includes the lidarunits 404-1, 404-2, 404-3, the radar units 406-1 406-2, 406-3, thetelematics device 420, the camera(s) 402, and the computing device 430(e.g., a computing entity 110), such as an edge node. The lidar (LightDetection and Ranging) units 404 are sensors that detect objects andbuild a map of a geographical environment based on transmitting aplurality of light pulses a second and measure how long it takes forthose light pulses to bounce off of objects in the environment back tothe sensor (e.g., 150,000 pulses per second). These lidar units, such as404-1, can indefinitely spin transversely in a plane parallel to theground capturing a 360-degree image of the logistics vehicle 120'ssurroundings. The output is a three-dimensional mapping of thegeographical environment. These sensors can also calculate the distancebetween itself and the objects within the environment, as well asdetecting exact sizes, colors, shapes of objects, and/or other metadata.

The radar units 406 are similar to the lidar units 404 in that they alsotransmit signals and measure how long these signals take to bounce offobjects back to the sensor. However, these signals are radio waves,instead of light pulses (which are faster). These sensors detect roaddynamics, such as detours, traffic delays, vehicle collisions, and otherobjects. Long range radar typically detects objects further awaycompared to lidar, which can be used for adaptive cruise control and thelike. Whereas lidar typically detects objects that are less far away andis used for emergency braking, pedestrian detection, collisionavoidance, etc.

The one or more cameras 402 utilize object recognition or computervision algorithms to detect and classify objects on the road, such aslane lines and traffic signs. These cameras can provide images to thecomputing device 430 for determining depth of field, peripheralmovement, and dimensionality of objects. In some embodiments, thesecameras 402 use deep learning or other machine learning models andtechniques for object classification. For example, in some embodiments,convolutional neural networks (CNN) are used to detect and classifyobjects, such as determining and classifying objects (e.g., car, person,traffic light, etc.). The one or more cameras 402 can be used forshort-distance recognition, such park assistance, compared to othersensors, such as lidar.

The telematics device 420 is configured to control a variety of vehiclesensors, collect vehicle telematics data generated by sensors, andtransmit the telematics data to the one or more analysis computingentities 105 and/or the computing entities 110 via one of severalcommunication methods. In various embodiments, the logistics vehicle 120is equipped with one or more vehicle sensors (e.g., the vehicle's enginespeed sensor, speed sensor, seat belt status sensor, direction sensor,and location sensor). These sensors can detect one or more of thefollowing attributes: engine ignition (e.g., on or off), engine speed(e.g., RPM and idle time events), vehicle speed (e.g., miles per hour),seat belt status (e.g., engaged or disengaged), vehicle heading (e.g.,degrees from center), vehicle backing (e.g., moving in reverse or notmoving in reverse), vehicle doors (e.g., open or closed), vehiclehandles (e.g., grasped or not grasped by a driver), vehicle location(e.g., latitude and longitude), distance traveled (e.g., miles betweentwo points), use of portable data acquisition device (e.g., in use ornot in use), throttle position, brake pedal position, parking brakeposition, and other measurements (e.g., engine oil pressure, enginetemperature, or engine faults). These sensors described above may beconfigured, for example, to operate in any fashion suitable to generatecomputer-readable data that may be captured and transmitted by thetelematics device 420.

In some embodiments, the telematics device 420 includes one or more ofthe following components, which are not shown: a processor, alocation-determining device or sensor (e.g., GPS sensor), a real-timeclock, J-Bus protocol architecture, an electronic control module (ECM),a port for receiving and decoding data from the vehicle sensors in oneof the logistics vehicles 120, a communication port for receivinginstruction data, a radio frequency identification (RFID) tag, a powersource, a data radio for communication with a WWAN, a WLAN and/or aWPAN, FLASH, DRAM, and NVRAM memory modules, and a programmable logiccontroller (PLC). In an alternative embodiment, the RFID tag, thelocation sensor, and the PLC may be located in the logistic vehicle 120external to the telematics device 420. In various embodiments, thetelematics device may omit certain of the components described above. Itshould be understood that the telematics device may include any othersuitable components. For example, the telematics device may includeother types of communications components than those described above.

According to one embodiment, a processor is configured to capture andstore telematics data from one or more vehicle sensors (e.g., GPSsensor, lidar unit 404, radar unit 406-1, etc.) on a logistics vehicle120 upon the occurrence of one or more defined vehicle events. Theprocessor is configured such that any parameter measurable by the one ormore vehicle sensors may be defined as a vehicle event. The processor isalso configured to associate telematics data received from the vehiclesensors 410 with contextual data indicating, for example: (1) the timethe data was captured (e.g., through time-stamping), (2) the vehicle thedata was captured from, (3) the driver of the vehicle, (4) a log reasonfor capturing the data, and/or (5) the route the driver was on at thetime the data was collected. In various embodiments, the processor isfurther configured to transmit the telematics data to the computingentity 110 and/or the one or more analysis computing entities 105. Inother embodiments, the processes described herein as being carried outby a single processor may be accomplished by multiple processors.

In one embodiment, the location sensor, which may be one of severalcomponents available in the telematics device 420, may be compatiblewith a low Earth orbit (LEO) satellite system or a Department of Defense(DOD) satellite system (e.g., via the satellite 112). Alternatively,triangulation may be used in connection with various cellular towerspositioned at various locations throughout a geographic area in order todetermine the location of the logistics vehicle 120 and/or its driver.The location sensor 202 may be used to receive position, time, and speeddata. It will be appreciated by those skilled in the art that more thanone location sensor 202 may be utilized, and that other similartechniques may likewise be used to collect geo-location informationassociated with the logistics vehicle 120 and/or its driver.

In some embodiments, the ECM with J-Bus protocol may be one of severalcomponents available in the telematics device 420. The ECM, which may bea scalable and subservient device to the telematics device 420, may havedata processor capability to decode and store analog and digital inputsand ECM data streams from vehicle systems and the sensors. The ECM mayfurther have data processing capability to collect and present vehicledata to the J-Bus (which may allow transmittal to the telematics device420), and output standard vehicle diagnostic codes when received from avehicle's J-Bus-compatible on-board controllers or vehicle sensors.

In some embodiments, an instruction data receiving port may be one ofseveral components available in the telematics device 420. Embodimentsof the instruction data receiving port may include an Infrared DataAssociation (IrDA) communication port, a data radio, and/or a serialport. The instruction receiving data port may receive instructions forthe telematics device 420. These instructions may be specific to thelogistics vehicle 120 in which the telematics device 420 is installed,specific to the geographical area in which the logistics vehicle 120will be traveling, or specific to the function the logistics vehicle 120serves within the fleet.

In some embodiments, a radio frequency identification (RFID) tag may beone of several components available for use with the telematics device420. One embodiment of the RFID tag may include an active RFID tag,which comprises at least one of the following: (1) an internal clock;(2) a memory; (3) a microprocessor; and (4) at least one input interfacefor connecting with sensors located in the logistics vehicle 120 or thetelematics device 420. Another embodiment of the RFID tag may be apassive RFID tag. One or more RFID tags may be internal to thetelematics device 420, wired to the telematics device 420, and/orproximate to the telematics device 420. Each RFID tag may communicatewirelessly with RFID interrogators within a certain geographical rangeof each other. RFID interrogators may be located external to thelogistics vehicle 120 and/or within the computing entity 110 that can becarried in and out of the logistics vehicle 120 by the vehicle operator.

In some embodiments, vehicle performance and tracking data collected bythe telematics device 420 (e.g., telematics data) may be transmitted viaa WPAN to, and stored by, the computing entity 110 until a communicationlink can be established between the computing entity 110 and theanalysis computing entity 105, or similar network entity or mainframecomputer system. In one embodiment, the computing entity 110 may displaytelematics data for the driver's viewing, which may be helpful introubleshooting vehicle performance problems and showing delivery routeprogress and instructions. In an alternative embodiment, the computingentity 110 may be a hand-held data acquisition device, like an iPAQ. TheMedia Access Control (MAC) address, which is a code unique to eachBluetooth™-enabled device that identifies the device, similar to anInternet protocol address identifying a computer in communication withthe Internet, can be communicated to other devices in communication withthe WPAN, which may assist in identifying and allowing communicationamong vehicles, cargo, and portable data acquisition devices equippedwith Bluetooth™ devices.

In some embodiments, the telematics device 420 receives the sensor datafrom the mapping sensors, such as the lidar units 404, radar sensors406, and camera 402 in order to provide more accurate predictions forvehicle events or more accuracy for mapping. For example, in someembodiments, the telematics data described above is combined with thismapping data from the sensors to provide additional insights, such asaverage speed of the logistics vehicle 120. These rich insights can beused for more than typical telematics sensors. For example, with richmapping data determined from the camera(s) 402 and the lidar units 404,it can be determined exactly when and where drivers took left turns orother maneuvers that either conform to or violate company protocols.These extra insights allow for significantly more monitoring of drivers,vehicles, and the way they react to the environment they are in. All ofthe information obtained from the telematics device 420 can be outputfor display on a user interface of the computing entity 110.

In various embodiments, some or all of the data derived from thesensors/devices described with respect to FIG. 4 is used as input into atime-based prediction model or otherwise used to determine randomnesscompensating factors and associated patterns for making predictionsdescribed herein. For example, each of the data collected from thetelematics device 420 and/or the lidar units 404, radar sensors 406,and/or the camera 406 are used to make a time-series prediction ofwhether the driver of the vehicle 120 is conforming to specified workprotocols (e.g., whether he is always taking right turns instead of leftturns, whether he has his seatbelt on, whether he is taking the bestroutes, whether he deviates from the scope of his work, etc.).Alternatively or additionally, the vehicle 120 described with respect toFIG. 4 is used as an output of making a time-based prediction using oneor more randomness compensating factors. For example, in response tomaking a prediction of a certain pickup or sorting volume at a facility,embodiments (e.g., the one or more analysis computing entities 105) cansend a control signal to the logistics vehicle 120, which causes thelogistics vehicle 120 to drive (via the lidar units 404, radar sensor406, and the camera 406) to the specific facility in preparation toreceive the volume of parcels predicted at the facility.

In some embodiments, the logistics vehicle 120 is a fully or partiallyautonomous vehicle for object detection. A “partially autonomousvehicle” or “semi-autonomous vehicle” as described herein is a vehiclewhere at least one function is human or operator-controlled in a manualmanner and at least one function is autonomous in that it is controlledby the autonomous vehicle without human control or intervention. Forexample, a partially autonomous vehicle can be a vehicle where thecruise control or regular braking is manually performed by a human butautonomous functions can include emergency braking or sendingnotifications about potential hazards that are out of sight of the humaneyes in the same vehicle. An “autonomous vehicle” or fully autonomousvehicle is a vehicle where no human control or interaction is required,such as braking, turning, cruise control, etc.

FIG. 5 is a block diagram of a system 500 for making a time-basedprediction, according to particular embodiments. The system 500 isgenerally responsible for making one or more time-based predictions andproviding a final output (e.g., a user interface). In some embodiments,the randomness compensating model 505, the presentation layer 516, theoutput time-based prediction 514, and/or the control signal propagator518 is included in the one or more analysis computing entities 105 ofFIG. 1. In other embodiments, some or each of the components in thesystem 200 are distributed or dispersed among some or each of thecomponents of FIG. 1 (e.g., the mobile computing entity 110, thelogistics vehicle 120, and the analysis computing entity 105). In someembodiments, the input data 502 is any data received from the datacorpus 160, the logistics vehicle 120 (e.g., via the telematics deviceor other sensors), and/or the one or more mobile computing entities 110.

The input data 512 represents any suitable data that is fed or providedto the randomness compensating model 504 such that the randomnesscompensating model 504 can make time-based predictions based on theinput data 512 it receives. In some embodiments, the input data 512includes historical induction volume from different logistics facilitiesat different time periods (e.g., observed volume at particular timeslices). Historical induction volume for instance can include thequantity of parcels (e.g., packages, crates, containers, bags of items,envelopes, etc.) processed through a logistics store or sorting facilityon a given day/week/month/year. In the shipping industry, before aparcel reaches a final delivery destination, it typically goes throughvarious operations. For instance, after a package has been dropped offat a carrier store for a delivery request (or ordered from an onlinemerchant), it may be routed to a sorting facility where the packagetraverses various different conveyor belt assemblies and processes inthe sorting facility based on information associated with the package(e.g., size of package, destination address, weight, etc.). Aftertraversal of the package through the sorting center, the package may beloaded into a logistics vehicle for delivery to the final deliverydestination or delivery to the next sorting phase operation. In variousinstances, it may be desirable to predict the volume or quantity ofparcels that will be processed at the logistics store or sortingfacility in order to prepare resources (e.g., conveyor belt assembliesor carrier personnel numbers).

In some embodiments, the input data 502 additionally or alternativelyincludes one or more of: parcel received time (e.g., the actual time oneor more parcels are received at a sorting operation facility), manifestparcel time, parcel information such as tracking number, parcel activitytime stamp, parcel dimension including height, length and/or width,parcel weight, parcel manifested weight (e.g., the weight of a parcel asindicated in a parcel manifest), parcel manifest time stamp (e.g., thetime at which a parcel manifest is uploaded), parcel service type,parcel scanned time stamp (e.g., the time at which a parcel was scannedto capture parcel information data), parcel tracking number, parcel sorttype code, parcel scanned code (e.g., a barcode), unit load device typecode, account number associated with the parcel, and the like. A “unitload device type code” identifies an entity type in which one or moreparcels are loaded into for delivery, such as a container, a deliveryvehicle, a bag, a pallet, etc.

The term “parcel manifest” refers to a report (e.g., a digital document)provided by a shipper to a shipping service provider that summarizes theshipment information about one or more parcels that the shipper is goingto provide to the shipping service provider. A parcel manifest mayinclude one or more of: the shipper's account information, shippingrecord identifier, dimensions of the parcel to be picked up, a plannedparcel pick up time, a parcel pick up location, parcel weight, trackingnumber, manifest time stamp (e.g., day of week, month, week, and/or hourthat manifest is uploaded), service type code, the like. A parcelmanifest may include any of the information described in the presentdisclosure.

The term “manifest parcel time” refers to the planned parcel pick uptime (e.g., by a carrier) and/or planned drop off time (e.g., by ashipper to a carrier) as indicated in the parcel manifest. For example,a shipper may request that a shipping service provider send a driver topick up a package at a certain location (manifest package location) at amanifest package time by selecting or inputting the time in a manifestpackage time field of the package manifest.

The term “parcel received time” refers to the actual time where thepackage is received by a shipping service provider or carrier from ashipper. For example, the package received time may be when carrierpersonnel at a shipping store print out a label for a package that ashipper has brought to the shipping store.

The term “parcel activity time stamp” refers to a time stamp generatedbased on the time-stamp data acquired when performing one or more parcelactivities. Parcel activity time stamps are indicative of times (e.g.,clock-times) at which one or more parcels are received and/ortransmitted to/from one or more locations. For example, a package timeactivity time stamp may be one or more of the following: a time stampgenerated when the package is received from the shipper, a time stampgenerated when the package is sent from a receiving site (e.g., asorting facility) to an intermediate transmit vehicle (e.g., anairplane), a time stamp generated when the package is sent from anintermediate transmit vehicle to another vehicle (e.g., the vehicle107), and the like.

The term “service type” or “parcel service type” refers to thecategorization of the service provided associated with the parcel. Forexample, service type may be categorized by delivery speed, returnreceipt requested, insurance associated with the package, originatinglocation, destination location, and the like. Exemplary service typesinclude “Next Day Air”, “2nd day Air”, “Worldwide Express”, “Standard”,and the like. In some embodiments, the service type is input or selectedwithin the package manifest by a shipper.

The term “sort type” or “parcel sort type code” refers to thecategorization of time in hours/minutes of package received time. Anexemplary way of defining sort type is provided as the following:

Package received between 10:00 pm and 5:00 am: Sort type “Late night”;

Package received between 5:00 am and 8:00 am: Sort type “Early Morning”;

Package received between 8:00 am and 2:00 pm: Sort type “Morning toearly afternoon”;

Package received between 2:00 pm and 10:00 pm: Sort type “Afternoon toNight”.

In some embodiments, the input data 502 additionally or alternativelyincludes sensor data obtained by the logistics vehicle 120. For example,the input data 502 can include sensor readings collected by thetelematics device 420 or any other sensor on the logistics vehicle 120.In some embodiments the input data 502 additionally or alternativelyincludes data from the data corpus 160 of FIG. 1. Additionally oralternatively, the input data 502 includes data input at the one or morecomputing entities 110.

The randomness compensating model 504 is generally responsible forgenerating the output time-based prediction 514 based at least in parton the input data 502 via the base time series forecasting model 510,the randomness compensating factor engine 506, and the residual randomfactor generator 512.

The base time-series forecast model 510 is generally responsible formaking time-based predictions. In various embodiments, this model is orincludes any suitable model, such as an Autoregressive Integrated MovingAverage (ARIMA) model, a Trigonometric Exponential smoothing State SpaceModel (TBATS), Prophet, XGBoost gradient learning, AutoML, Naiive, aHidden Markov Model, and/or the like. These models have some level ofaccuracy in making time-based predictions but not enough accuracy sincethey do not account for patterns in noise or apparent randomness.Accordingly, various embodiments augment or add data derived from therandomness compensating factor engine 506 to the output prediction ofthe base time-series model, as described in more detail below.

The randomness compensating factor engine 506 is generally responsiblefor modifying a prediction made by the base time series forecastingmodel 510 and/or making a new prediction relative to the time seriesforecasting model 510. The randomness compensating factor engine 506does so by determining or extracting the randomness compensating factor1-n (i.e., one or more) 508, determining one or more patterns associatedwith the randomness compensating factor(s) 508 via the randomnesscompensating factor pattern generator 509, and responsively using theprediction modifier 530 to modify the prediction made by the base timeseries forecasting model 510.

The one or more random compensating factors 508 or any “randomnesscompensating factor” described herein refers to any feature(s),attribute(s), or other phenomena for which there can be an observationmade at certain associated time slices (e.g., days, weeks) in order tomake one or more predictions at a future time slice(s) and in order toreduce randomness. Randomness compensating factors are typically datapoint (e.g., sorting center) specific in that some (or different)randomness compensating factors are used for some data points but notothers. For example, a randomness compensating factor can be a locationof a logistics facility, particular identity or amount of businesseswithin a threshold distance of the logistics facility, seasonality ofthe logistics facility local and national holiday in an area of thelogistics facility, sale events at or near the logistics facility,business recalls at or near the logistics facility, weather in the samecity as the logistics facility, customer behavior (e.g., via parcelmanifest) leading up to or at the logistics facility, geo-politicalevents (e.g., voting event for local elections), operational failuresneed volume to be handled by neighboring centers, and the like. Aparticular observation (e.g., parcel volume) can be made at theparticular time slices (also referred to herein as “time intervals” or“time periods”) (e.g., days, weeks, months, and/or years) that thesefactors occur on in order to make future predictions. For example, usingthe illustration above, a “sale event” at a logistics store A may occurevery Monday. In some embodiments, the one or more randomnesscompensating factors are stored in the input data 502.

The randomness compensating factor engine 506 may extract or determinethe one or more randomness compensating factors 508 using any suitableuser selection or automated functionality. For example, in someembodiments the one or more randomness compensating factors 508 aredetermined in response to receiving user input (e.g., a UI selection orstring formulation) that specifies the randomness compensating factor(s)508. For instance, embodiments can receive a UI user selection of abutton “randomness compensating factor,” where the user specifies thetype: “geopolitical event-local election,” and specifies the timeslice(s) that it will occur on as “Friday, March 22^(nd).” Alternativelyor additionally, in some embodiments the one or more randomnesscompensating factors 508 (or their values) are extracted or determinedvia one or more automated algorithms without user input. For example, inresponse to a data point being specified, such as a first center,embodiments can automatically extract values for a predetermined set ofrandomness compensating factor types. For example, using theillustrations above, embodiments can extract: a logistics facilitylocation, a particular identity or amount of businesses within athreshold distance of the logistics facility, a seasonality of thelogistics facility local and national holiday in an area of thelogistics facility, sale events at or near the logistics facility,business recalls at or near the logistics facility, weather in the samecity as the logistics facility, customer behavior (e.g., via parcelmanifest) leading up to or at the logistics facility, geo-politicalevents (e.g., voting event for local elections), operational failuresneed volume to be handled by neighboring centers, and the like.

The randomness compensating factor pattern determiner 509 is generallyresponsible for associating the one or more randomness compensatingfactors 508 with one or more time-based patterns with regard to ahistorical observation for a particular time slice or set of timeslices. The randomness compensating factor pattern determiner 509additionally or alternatively determines the one or more time-basedpatterns with regard to the historical observations. For example, someembodiments perform harmonics synthesis or otherwise identifyharmonics-type patterns in particular observations over some timeperiod. A waveform is a periodic mathematic function(s) defined by afrequency, amplitude, and phase. Frequency is the number of full cyclesor that the waveform goes through in one second (or time period). Invarious instances, a harmonic is a waveform with a frequency that is aperfect integer multiple of the frequency of any given waveform. Whencertain observations are made, harmonics can be generated between timeslices for each observation made, which is indicative of a pattern ofobservations. For example, embodiments can determine that there hashistorically been a volume between 100 and 1015 on a particular holidayeach year. However, for every other day of the week that the holiday ison, there is a volume between 2000 and 3000. Accordingly, there issubtractive or negative pattern of waves (i.e., observations) that canbe visualized for every holiday via harmonics synthesis, which canappear to be noise by other time series forecasting models, which woulduse the majority data from the non-holidays.

In some embodiments, the randomness compensating factor patterndeterminer 509 adds the harmonics of a fundamental sinusoidal waveformto each other and to the fundamental waveform itself. This is known as“additive synthesis.” Each harmonic may have a different amplitude(level) and phase. By varying these properties and adding them together,the randomness compensating factor pattern determiner 509 can generateany type of waveform. Conversely, the randomness compensating factorpattern generator 509 removes the harmonics of fundamental sinusoidalwaveform to each other, which is known as “subtractive synthesis.” Byvarying these properties and subtracting them one from another, can alsomodify or generate any type of waveform.

In some embodiments, the randomness compensating factor patterndeterminer 509 alternatively or additionally determines patterns basedon using one or more machine learning algorithms. For example, someembodiments use a deep learning neural network (e.g., a ConvolutionalNeural Network (CNN), Recurrent Neural Network (RNN), random forestregression, or the like to learn patterns and associations for theobservations for particular time periods. Accordingly, machine learningalgorithms may parse or extract features of historical data (e.g.,features from the input data 502), learn (e.g., via training) about thehistorical data by making observations or identifying patterns in data,in order to make a determination, prediction, and/or classification ofthe subsequent input based on the learning without relying onrules-based programming (e.g., conditional statement rules).

In an illustrative example of how harmonics synthesis works or how therandomness compensating factor pattern determiner 509 functions,embodiments can identify every week (or other time period) of the yearto a known week pattern from a set of 11 to 13 typical weeks of theyear, for example. From historical data, it is understood how certaincustomers are affected/influenced in those weeks. Weather incidents cancorrespond to a set of data points. Local events and regional events andtheir influence on the volume can be another set of data points.Customer's self-declared forecast to logistics entities can be yetanother set of data points. Using all this data, we determine theunderlying frequencies that resulted in the harmonics. For example,particular embodiments can apply pattern recognition (e.g., via one ormore machine learning models) and/or clustering analysis (e.g., via aclustering model, such as k-means, connectivity models, graph-basedmodels, etc.) to identify 11-13 sets of unique weekly ratios by day ofweek. For instance, Easter, January 1st week, Normal week, Mondayholiday week, July 4th, Thanksgiving, Cyber week, Peak week 2, Peak week3, Christmas and New Year Eve, Prime day, singles day.

The prediction modifier 530 is generally responsible for modifying theprediction made by the base time series forecasting model 510 (or makinga new prediction) based on the randomness compensating factor(s) 508that have been identified and the pattern(s) extracted via therandomness compensating factor pattern determiner 509. In someembodiments, the prediction modifier 530 modifies the prediction byadding or subtracting prediction values via weighting or the like. Insome embodiments, “weighting” includes adding or subtracting predictionvalues directly proportional to the difference between the predictedvalues made by the base time series forecasting model 510 (or observedvalues for non-randomness compensating factors) and past observed valuesfor the randomness compensating factor(s) 508. For example, if the basetime series forecasting model 510 predicted that there would be 43 saleson this coming Tuesday based on averaging the last three observedTuesday sales of 55, 52, and 22 but the randomness compensating factorpattern determiner 509 determined that a randomness compensating factor(e.g., a region-wide geopolitical event) occurred on the same date asthe date of the 22 sales and would not occur for the upcoming predictedTuesday date, then the prediction modifier 530 can weight 43 to a highervalue (e.g., 50) based on the proportion or percentage differencebetween 43 and 22. This is because, for example, the geopolitical eventor other randomness compensating factor may only occur during certaintime periods but will not occur in other future time periods, thuspotentially affecting observed values. For instance, if most people in atown attended a geopolitical event on a Tuesday, they would not havetime to ship parcels, and so the volume would be lower. Because thegeopolitical event may be annual or otherwise not occur often, this datapoint should not be used for predictions or should otherwise bemodified.

Alternatively or additionally, in some embodiments, the predictionmodifier 530 modifies the base time series forecasting model 510prediction itself by completely removing (or adding) observations thatare a part of a randomness compensating factor. For example, using theillustration above, instead of weighting, embodiments can completelyremove the observed Tuesday sale of 22 such that the average or otheraggregation calculation does not take into account the value of 22. Forinstance, embodiments can average 55 and 52 to arrive at a predictedvalue of 52.5 (instead of 43).

The residual randomness determiner 512 determines residual randomnessbetween the prediction made by the prediction modifier 530 and theactual observed values. “Residual randomness” as described herein refersto the difference between observed values (e.g., historical volume countfor a Monday) and a predicted value (e.g., predicted volume count for afuture Monday). The “predicted value” in some embodiments represents theprediction made by the prediction modifier 530, as described above.Additionally or alternatively, residual randomness corresponds tophenomena, features, or the like for which the randomness compensatingfactor pattern determiner 509 is not able to determine a pattern for.

In some embodiments, the residual random factor determiner 512determines residual randomness by performing regression and analysis tocreate a confidence level for the aggregate forecast (i.e., the outputtime-based prediction 514). In these embodiments, the residualrandomness equates to the vertical distance (or distance in observation)between a data point (i.e., an actual observation) and a correspondingportion of a regression line representing the predicted value for thesame time slice. For example, for time slice A (Saturday), it may bepredicted that 30 personnel will arrive at a particular location, butthe actual personnel arrival may have been 20. The residual is thus10—the difference between 30 and 20. In this way, the residualrandomness determiner 512 can generate a confidence interval orotherwise provide a probability that a parameter or prediction valuewill fall between two set values for a certain proportion of times. Forinstance, there can be a 95% probability that the predicted value willbe between 30 and 20.

The output time-based prediction 514 represents the prediction made bythe prediction modifier 530, along with the confidence intervalgenerated by the residual randomness determiner 512. In someembodiments, the output time-based prediction 514 is illustrated by thefollowing formula:

${F(t)} = {{F_{b}(t)} + {\underset{i = 0}{\sum\limits^{i = n}}{f_{i}(t)}} + {R(t)}}$

where F(t) represents a forecast for a particular domain for the timeframe t (final forecast), and where Fb(t) represents a base time seriesforecasting model prediction for the particular domain (as generated bythe base time series forecasting model 510), and where f_(i)(t)represents a randomness compensating factor (one of the randomnesscompensating factor(s) 508) for a domain that is not part of the baseforecast up to n factors for the particular domain, and where R(t)represents residual randomness (as determined by the residual randomnessdeterminer 512) that is not modeled for the particular domain using arandomness compensating factor.

In some embodiments, the output time-based prediction 514 is provided(e.g., via an API) to a presentation layer 516. The presentation layer516 is generally responsible for structuring, tagging, or otherwiseformatting the output time-based prediction 514 for presentation (e.g.,to a service or user device). For example, the presentation layer 516can cause display of a user interface of a user device that includes theprediction made by the prediction modifier 530 and the confidence levelgenerated by the residual randomness determiner 512.

In some embodiments, the randomness compensating module 504communicates, via an application programming interface (API), to thepresentation layer 516 and/or the control signal propagator 518. In someembodiments, the presentation layer 516 includes one or moreapplications or services on a user device (e.g., the mobile computingentity 110), across multiple user devices, or in the cloud. For example,in one embodiment, presentation layer 516 manages the presentation ofcontent to a user across multiple user devices associated with thatuser. Based on content logic, device features, and/or other user data,presentation layer 516 may determine on which user device(s) content ispresented, as well as the context of the presentation, such as how (orin what format and how much content, which can be dependent on the userdevice or context) it is presented, when it is presented. In particular,in some embodiments, presentation layer 516 applies content logic todevice features, or sensed user data to determine aspects of contentpresentation.

In some embodiments, presentation layer 516 generates user interfacefeatures. Such features can include interface elements (such as graphicsbuttons, sliders, menus, audio prompts, alerts, alarms, vibrations,pop-up windows, notification-bar or status-bar items, in-appnotifications, or other similar features for interfacing with a user),queries, and prompts. For example, the presentation layer 516 canpresent the one or more randomness compensating factors 508, one or morepatterns determined by the randomness compensating factor patterndeterminer 509, the predictions made by the prediction modifier 530(and/or the base time series forecasting model 510), and/or theconfidence level. In some embodiments, the presentation layer 516presents warnings, notifications, or other alerts based on thepredictions made by the prediction modifier 530. For example, thepresentation layer 516 can cause a notification to be displayed to themobile computing entity 110, which alerts carrier personnel that theyneed to travel to a logistics facility, inform others, or otherwiseprepare for a predicted volume at a particular time period based on thepredictions made.

Alternative or in addition to the presentation layer 516 functionality,in some embodiments the randomness compensating module 504 (or module)communicates, via an API, to the control signal propagator 518 (e.g.,when the output time-based prediction 514 has surpassed a particularthreshold). This communication causes the control signal propagator 518to send a control signal to a machine, apparatus, or article ofmanufacture, which effectively and tangibly causes such machine,apparatus, or article of manufacture to activate or otherwise perform aparticular function. For example, if it is predicted that carrierpersonnel will not conform to driving protocols (e.g., via the inputdata 502 that includes telematics device 420 data, such as sensor dataindicating whether driver is wearing a seatbelt) during a certain timeperiod, the control signal propagator 518 can send a control signal tothe logistics vehicle 120 to completely stop or deactivate the logisticsvehicle 120 such that the driver cannot drive the logistics vehicle 120.In another example, if it is predicted that the volume will surpass athreshold at a sorting center, then the control signal propagator 518can send a control signal to the logistics vehicle 120 so that thelogistics vehicle 120, being autonomous in some embodiments, cantraverse to a docking station at the sorting center in preparation toreceive the predicted volume for final-mile delivery. As describedabove, in some embodiments, the logistics vehicle 120 includes a droneor AAV and so the control signal can be sent to these apparatuses.Additionally or alternatively, the control signal propagator 518 cansend a control signal to a conveyor belt apparatus within the sortingcenter thereby causing the conveyor belt to speed up or slow down inresponse to receiving the predicted volume at the sorting center.

In yet other examples, the control signal propagator 508 can send acontrol signal to a computing device (e.g., the mobile computing entity110) causing an auditory (e.g., a beeping sound), visual (e.g., flashingLEDs), buzzing/vibrating, and/or other alert type based on the outputtime-based prediction 514 being over some threshold. For example, inresponse to the volume predicted to be over a threshold, the controlsignal propagator 518 can cause a control signal to be sent to aconveyor apparatus, DIAD, a component within the logistics vehicle 120,or other item, which emits an auditory sound to alert an operator oruser that attention is needed based on the output time-based prediction514.

IV. Exemplary System Operation

FIG. 6 is a schematic diagram 600 illustrating how a compositeobservation can be broken down into a base model, randomnesscompensating factor patterns, and residual randomness, according to someembodiments. In some embodiments, the diagram 600 can be provided orpresented to a computing device (e.g., via the presentation layer 516 ofFIG. 5). The diagram 600 generally represents different historicalobservations (e.g., specific parcel volume quantities processed) atspecific values (Y-axis) over a time period (X-axis).

The row 602 illustrates a composite observation with seemingly randomphenomena. The “composite” observations corresponds to raw observationsover a time period without the data having gone through any time-basedmodels, normalizing, filtering, and the like. As illustrated, the dataappears very noisy and appear not to have any patterns in some portions.

The row 604 illustrates a time-based model (e.g., the base time seriesforecasting model 510) that represents the observed behavior but it doesnot have factors to compensate randomness. As illustrated, only thegeneral or generic trends have been captured from the raw data in thecomposite observation, such as via average or other metric. Accordingly,this leaves out extreme outliers, such as the observation 602-2 relativeto the observation 602-1 for the same time slice.

Row 606 illustrates an indication of a first randomness compensatingfactor identified (e.g., randomness compensating factor 508), along withan apparent pattern (e.g., as determined by the randomness compensatingfactor pattern determiner 509) of observations over time—i.e., the datapoints 602-2, 602-3, 602-4, and 602-5. As is illustrated, these datapoint observations over this time period are at or near the same valueafter nearly the same amount of time that goes by, which is nearlycompletely opposite of the data points in the composite observations(e.g., data pint 602-1). As illustrated, these are “subtractive” (e.g.,subtractive harmonics synthesis) or have negative patterns/trends basedat least in part on the observations being lower than the base model forthe same time slices.

Rows 608, 610, and 612 all illustrates indications of other randomnesscompensating factors, along with associated patterns of observationsover time—the same time slices as the model and other randomnesscompensating factors. As illustrated, these are “Additive” (e.g.,additive harmonics synthesis) or have positive patterns/trends based atleast in part on the observations being higher than the base model forthe same time slices.

Row 614 illustrates residual randomness (e.g., as determined by theresidual randomness determiner 512), which represents true randomness.Although residual randomness is not modeled, the effect is significantlyreduced, as described above with respect to FIG. 5. As illustrated,there is no apparent pattern of observations for the same time slices asthe base models or randomness compensating factors.

FIG. 7 is a time-series graph 700 that specifically illustrates a basemodel volume observation relative to a volume observation associatedwith a particular randomness compensating factor for the same timeslices, according to some embodiments. In some embodiments, FIG. 7represents a portion of the diagram of FIG. 6. For instance, data point703 may represent or be included in the data point 602-2 and data point705 may represent or be included in data point 602-1. A “data point” inthis context represents a particular observation made at a particulartime slice. A data point may additionally refer to a predicted value(not just observed value) for a particular time slice.

As illustrated in the time-series graph 700 for data point 705, the basemodel observes that there were 8000 parcels processed at a logisticsfacility for time slice T1 (e.g., clock time, day, week, month, year,etc.) and for data point 709, around the same number processed (e.g.,7090) at time slice T2. Further, for data point 703, embodiments (e.g.,the randomness compensating factor pattern determiner 509) observes thatthere were only 1000 parcels processed at the logistics facility for thesame slice T1 (e.g., the same clock time on a different day, the sameday of a different week, the same week for a different year, etc.). Fordata point 707, around the same number processed (e.g., 1050) at timeslice T2. As described above, certain embodiments (e.g., the predictionmodifier 530) can take the difference between the observations 8000 and1000 at T1 (or all of the observations for each time slice T1 and T2)and weight or aggregate the prediction accordingly. For example, someembodiments average the observations 8000 and 1000 to arrive at a newpredicted value of 4,500. Some embodiments weight the 8000 observationalvalue only slightly lower (as defined by a particular percentage, suchas 1% or 2%) based on the difference being over a threshold or theprobability that the local event randomness compensating factoroccurring at some future time interval. For example, 8000 can bemultiplied by 0.02 (2%), in which the raw number 160 is subtracted from8000 to arrive at a number of 7,840 if it is predicted that at timeslice T3 (a future time where no observations can be made), the localevent will not occur. Conversely, 8000 can be multiplied by 0.6, inwhich the raw number 4,800 to arrive at a number 3,200 if it ispredicted that at time slice T3, the local event will occur. In someembodiments, this prediction of whether the local event will or will notoccur at T3 is based on the patterns identified (e.g., by the randomnesscompensating factor pattern determiner 509). For example, thetime-series graph 700 may indicate a pattern between T1 and T2 in thatthere have been observed values of around 1000. Accordingly, it can bepredicted that for the same future time slice T3 (e.g., the same day ona different week), that the volume will be closer to 100. Accordingly,some embodiments responsively weight the base model projection closer tothe predicted value for T3 for the randomness compensating factorassociated with the local event (e.g., a local sale, local holiday,local political event, etc.). The reverse is true if T3 was not a sametime slice as T1 and T2 (e.g., it was a different day of the week), suchthat the weighting would stay closer to the base-model prediction or noweighting would be used at all.

FIG. 8A is a schematic diagram of an example exponential smoothingforecast model table 800, according to some embodiments. In someembodiments, the forecast in FIG. 8A represents a prediction made by thebase time series forecasting model 510 of FIG. 5. Although the table 800includes specific values, calculations (e.g., WMAP), and time sequences(day 1-5), it is understood that this is representatively only and thatany set of values, calculations, and/or time sequences can exist. Forexample, instead of or in addition to making volume forecasts for aparticular set of “days,” there may be forecasts for a particularsequence of months, years, weeks, and/or any other time period sequence.In another example, instead of or in addition to calculating WMAPE(weighted mean absolute percent error), other model accuracy validationmethods can be used, such as root mean square error (RMSE), meanabsolute percent error (MAPE), mean square error (MSE), and/or any othersuitable error calculation mechanism. In various embodiments, the table800 (or similar table with the same calculations) is included in or usedwith one or more learning models. In some embodiments, the table 800represents a data structure stored in memory, such as a hash table. Insome embodiments, the table 800 is configured to be stored in memory andbe displayed (e.g., to the mobile computing entity 110) in response toor while generating output of a volume forecast

The table 800 illustrates what the volume forecast or prediction will befor days 1 through 5 for the logistics facility Y. The logisticsfacility Y can represent any suitable logistics facility, such as asorting center, logistics store, logistics vehicle, etc. These forecastscan be provided for multiple logistics facilities. It is understood thatalthough FIGS. 8A (and 8B, 8C, 8D, and 8D) represent “volume” observedvalues associated with logistics facility domains and forecasts, anysuitable observation and domain can be represented. For example, thedomain can be a particular retailer store or other facility where theobservation is the amount of sales that are used to forecast a certainnumber of sales. In another example, the domain can be a certain companyor entity where an observation is the stock price that is used toforecast a future stock price.

The particular values are populated within the table 800 based onexponential smoothing forecast algorithms. In various embodiments,generating a forecast or prediction for a particular day is generatedthrough the following expression: F_(t)+1=αA_(t) (1−α)F_(t), whereF_(t)+1 is a particular forecast/prediction of volume for a particulartime period (day) or forecast/prediction for the current time period,where a (alpha) is a value between 0 and 1 (i.e., the smoothingconstant), where A_(t) is the last actual volume value (e.g., actualquantity of received parcels) of the immediately preceding time period,and where F_(t) is the last forecast value (e.g., predicted quantity ofparcels that a facility will receive) of the immediately preceding timeperiod. For purposes of the specific values within the table 900, alphaα is assumed to be 0.2.

In an example illustration, at day three it may be currently unknown howmany “small” parcels will be received at sorting facility Y. However,the learning model may project that there will be 34.2 small parcelsthat will be received at sorting facility Y on day 3, as illustrated inthe table 800. Accordingly, using the expression above, the new forecastor forecast at day three (F_(t)+1)=(0.2)(43)+(0.8)(32), which equals34.2. That is, alpha 0.2 is multiplied by the last actual value A_(t) ofday 2, which is 43. The result is a value of 8.6. Then 0.8 (the value of1−α) is multiplied by 32, which is the last forecasted value F_(t) ofday 2 to arrive at a value of 25.6. Then 8.6 is added to 25.6 to arriveat the final result of 34.2. Accordingly, even though the current actualvolume value A_(t) of 56 may not be known at the time, it can beprojected that there will be 34.2 small parcels received at sortingfacility Y on day 3. Then at a later time, the actual value for day 3may be received, which is 56, may be used to make future forecasts (day4 and day 5). Day 5 illustrates a time period where the actual volumeA_(t) is currently unknown, but the forecasted value F_(t) is stillprojected to be 41.65 based on using the expression above.

The “Error,” “Error²,” and “WMAP” columns of the table 900 are utilizedto validate accuracy of the exponential smoothing forecast model. Thevalues of the “Error” column are calculated by subtracting theforecasted values from the actual values for each time period(A_(t)−F_(t)). For example, for day 2, A_(t) value of 43 is used tosubtract the F_(t) day 2 value of 32 to arrive at an “Error” value of11. The “Error²” values are calculated by squaring each of thecorresponding Error values for the same time period. For example, forday 2, the error value of 11 is squared to arrive at a value of 121. The“Error²” column can be used to generate other analyses, such as MSE,which is calculated by adding up each squared error of the table 800 anddividing this value by the total number of time periods (5 days). The“WMAPE” (weighted mean absolute percent error) is calculated via thefollowing expression:

$\frac{\sum{\frac{{A - F}}{A} \times 100 \times A}}{\sum A}$

where A represents A_(t) or the current volume value for a particularday and F represents F_(t) or the currently forecasted volume value forthe same particular day. For example, for day 2, the absolute value of43 (the actual volume value)−32 (the forecasted volume value) is dividedby 43 to arrive at 0.256. This value is then multiplied by 100 and 43 toarrive at the value of 1, 100.0, which is then divided by 43 to arriveat the WMAPE value of 25.6 for day 2. WMAPE is utilized to focus on orweight errors that have a relatively larger impact or little to noimpact at all. Standard MAPE calculations treat all errors equally,while WMAPE calculations place greater significance on errors associatedwith larger items by weighting these errors more.

FIG. 8B is a schematic diagram of an example time series graph 803associated with the table 800 of FIG. 8A. The graph 803 representsactual and forecasted volume predictions for different alpha values andactual values. In some embodiments, the “time” axis (X-axis) is orincludes days 1-5 as indicated in the table 800. For example, the “time”axis in the graph 803 can represent a larger time sequence, such as days1-90, where days 1-5 (as indicated in FIG. 8A) is only a portion of theoverall time sequence. The “volume” axis (Y-axis) represents the rawnumber or quantity (and projected quantities) of shipments or parcelsreceived or shipped. The time series instance 805 represents the actualvolume quantity received over a first time at a particular trend orslope. The time series instance 807 represents the projected volumequantity that will be received over the same first time at a first alphalevel (e.g., 0.7) at a particular trend. The time series instance 809represents the projected volume quantity that will be received over thesame first time at a second alpha level (e.g., 0.2) at a particulartrend. As illustrated in the graph 903, both the actual received volumeand the volume projections become considerably larger as the timeprogresses. In some embodiments, the graph 803 is configured to bestored in memory and be displayed (e.g., to the mobile computing entity110) in response to generating output of a volume forecast.

FIG. 8C is a schematic diagram of an example exponential smoothingforecast model table 800-1 with adjusted values relative to FIG. 8A,according to some embodiments. FIG. 8C represents adjusted forecastvalues in light of using the randomness compensating factor A. In someembodiments, the “adjusted forecast” is performed by the predictionmodifier 530 of FIG. 5. The table 800-1 illustrates that randomnesscompensating factor A occurs on both days 3 and 4 (e.g., a two-day salethat occurs these same two days every month). Accordingly, for day 3,the forecast value of 34.2 illustrated in the table 800 has beenmodified to 52. Likewise, for day 4, the forecast value of 38.56 hasbeen modified to 50. As illustrated in the table 800-1, the “error,”“error 2,” and “WMAPE” values are responsively modified based on theadjusted forecast relative to the table 800. Specifically, these errorvalues and the residual randomness in general of table 800-1 is loweredrelative to the corresponding values in the table 800. As describedherein, this is because the randomness compensating factor A has beendetermined. Accordingly, forecasted values can be weighted or otherwisechanged, as described herein.

FIG. 8D is a schematic diagram of an example time series graph 811associated with the table 800-1 of FIG. 8C. The graph 811 representsactual and adjusted forecasted volume predictions for different alphavalues and actual values similar to FIG. 8B. The time series instance813 represents the actual volume quantity received over a first time ata particular trend or slope. The time series instance 815 represents themodified projected volume quantity that will be received over the samefirst time at a first alpha level (e.g., 0.7) at a particular trend. Asillustrated, the instance 815 more closely follows time series instance813 relative to time instance 807 to 805 based on the reduced errorstatistics and reduced randomness. The time series instance 817represents the projected volume quantity that will be received over thesame first time at a second alpha level (e.g., 0.2) at a particulartrend.

FIG. 9 is a schematic diagram of a mobile device 900 indicating an alertthat is presented based on making a prediction, according toembodiments. In some embodiments, the mobile device 900 represents themobile computing entity 110 or any other user device. In someembodiments, the alert represents the notification that is pushed by thepresentation layer 516 to a mobile computing entity 110. Alternativelyor additionally, this represents an alert that is surfaced in responseto the projections made as illustrated in FIG. 6, FIG. 7, and/or FIGS.9C and 9D. The alert states “WARNING . . . you may have staff shortagesat facility Y for Saturday March 14^(th) based on the predicted volume.Click button for more details.”

In response to receiving the user selection of the button 903,particular embodiments generate and provide a more detailed or expandedview of particular information associated with the alert. For example,any element illustrated in FIG. 6, FIG. 7, FIG. 9C, and/or FIG. 9D canbe presented for display on the mobile device 900 such that the user cansee exactly what the projected volume is for certain dates, the residualrandomness, and other dates, etc. In response to receiving the userselection of the button 905, embodiments send a notification to otheruser devices associated with personnel at facility Y. For example,embodiments can send the alert (e.g., via email, SMS text, chat, etc.)to the manager at facility Y so that the manager can call or otherwisecommunicate with workers to have them work additional/longer shiftsbased on the alert.

In some embodiments, the alert on computing device 900 is provided by alogistics entity, such as by the analysis computing entity 105 (e.g.,over the network(s) 135 to the computing entity 110). In particularembodiments, the alert is provided to any suitable entity, such as oneor more of the computing entities 110, and/or the logistics vehicle 120.The alert can be accessed or provided in any suitable manner. Forexample, in some embodiments, a user can open a client application, suchas a web browser, and input a particular Uniform Resource Locator (URL)corresponding to a particular website or portal. In response toreceiving the user's URL request, an entity, such as the one or moreanalysis computing entities 105 may provide or cause to be displayed toa user device (e.g., a computing entity 110), the alert.

FIG. 10 is a flow diagram of an example process 1000 for generating aprediction, according to some embodiments. The process 1000 (and/or anyof the functionality described herein) may be performed by processinglogic that comprises hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructions runon a processor to perform hardware simulation), firmware, or acombination thereof. Although particular blocks described in thisdisclosure are referenced in a particular order at a particularquantity, it is understood that any block may occur substantiallyparallel with or before or after any other block. Further, more (orfewer) blocks may exist than illustrated. For example, in someembodiments the process does not include blocks 1009 and/or block 1013.Such added blocks may include blocks that embody any functionalitydescribed herein. For example, there may be an added block thatdescribes the functionality of the control signal propagator 518. Thecomputer-implemented method, the system (that includes at least onecomputing device having at least one processor and at least one computerreadable storage medium), and/or the computer readable storage medium asdescribed herein may perform or be caused to perform the process 800,and/or any other functionality described herein.

The process 1000 is directed using statistical techniques and patterndetermination methods (e.g., harmonics synthesis) to reduce the effectof randomness in forecast accuracy. In most cases, true randomnesscannot be modeled. When modeling a time series event, seeminglyappearing randomness may be broken down to basic patterns (e.g., asillustrated for data points 703 and 707 of FIG. 7 or rows 606, 608, 610,612, 614 of FIG. 6). Various randomness compensating factors that havedifferent patterns relative to each other or a base model can beaddressed independently (e.g., as shown in FIG. 6). The residualrandomness may be factored to improve the accuracy of an aggregateforecast (e.g., a forecast of several base time-series models). Certainembodiments describe a process/platform that improves forecast accuracyby determining an addition call a “randomness compensating factor,”which may influence traditional time series forecast models. Existingtechnology and applications do not forecast or predict events thatappear to have a high degree of randomness with randomness compensatingfactors to increase the forecast accuracy.

Per block 1003, some embodiments receive input data that includes one ormore observations made at corresponding time slice(s). For example,embodiments can receive sensor data from the logistics vehicle 120 (ormore specifically from the telematics device 420 or other computingdevice located on the logistics vehicle 120) where observations are madethat the logistics vehicle was at certain geo-coordinates (e.g.,latitude and longitude) at specific time slices (e.g., 4 p.m. and 6p.m.). Other examples of block 1003 are described with respect to FIG. 7where it is observed that certain parcel volume observations were madeat time slices T1 and T2. Other example of bloc 1003 are described withrespect to FIG. 8A where it is observed that there are certain “volume”observations made for each of the days 1 through 5.

It is understood that these observations and time slices described withrespect to block 1003 are representative only. As such, the input datamay be any suitable data where any conceivable observation can be madefor any event for any time period. For example, the input data can be orinclude any of the input data described with respect to the input data502. Additionally or alternatively, the input data can be any datagathered by any sensor or device located on the logistics vehicle 120.Additionally or alternatively, the input data can include the following:historical pickup volume by service level for various time slices,historical pickup volume by logistics facility for various time slices,IoT data on parcels with RFID or other sensors that indicate parcelmanifest or other parcel attributes for various time slices, commercialseasonality data, autonomous and smart vehicle data for various timeslices or sensor data captured from a logistics vehicle (e.g., thelidar, radar, and/or camera data received from the logistics vehicle120), telematics data (e.g., gathered from the telematics device 420 ofFIG. 4) captured from a logistics vehicle for various time slices, staffcapacity or staff count numbers for particular time slices for aparticular logistics facility, staff absence counts for particular timeslices, data captured from a parcel manifest (or any data associatedwith a parcel), and/or the like.

Per block 1005, particular embodiments provide the input data to atime-based prediction model. A “time-based prediction model” asdescribed herein is any suitable model that makes predictions based atleast in part on time or temporal factors. For example, the time-basedprediction model can be any suitable machine learning model (e.g., LongShort Term Memory (LSTM) networks, Recurrent Neural Networks (RNN),Multi-layer Perception (MLP)), time series forecasting model, or anyother model, such as Naive models, exponential smoothing models,ARIMA/SARIMA, and the like. In some embodiments, the one or moretime-based prediction models represent the one or more base time-seriesforecasting models 510 of FIG. 5. In various embodiments, the time-basedprediction models predict one or more future values (e.g., the“forecast” value of 32 in FIG. 8A) for one or more future time slices(e.g., day 1) based on the one or more time-based prediction modelsanalyzing the input data. For example, this is described with respect tothe time-series forecasting model(s) 510 of FIG. 5. An example of thisis also described with respect to FIG. 8A where various forecasts aremade for particular days.

Per block 1007, one or more randomness compensating factors aredetermined. In some embodiments, block 1007 is performed by therandomness compensating factor engine 506 as described herein. In someembodiments, the one or more randomness compensating factors correspondto one or more features indicative of one or more events that occur onone or more of the corresponding time slices. For example, the one ormore events can be any suitable event or factor described with respectto the one or more randomness compensating factors 508, the “local eventrandomness compensating factor” of FIG. 7, the randomness compensatingfactor “A” of FIG. 8D. Examples of such events include: a nationalholiday on a certain day of the year, a local holiday on a particularday each month, a bi-weekly sale event, weather data (e.g., specifictemperature or specific forecast of snow, rain, sun, pressure) at aparticular time period, a business recall event on a particular day, alocation of a sorting center, a geo-political event (e.g., a localvoting event) on a particular day over multiple years, the type ofbusiness near a domain, and the like.

In some embodiments, the one or more features corresponding to theevents are not indicated in the one or more base time-series forecastingmodels (or time-based prediction models). For example, as indicated inthe row 604, the primary model does not include or observe thesubtractive randomness compensating factors 602-2, 602-3, 602-4, 602-5.As indicated herein, some existing models determine that this historicalobserved phenomena corresponds to noise or randomness and so it is nottaken into account in particular models. Alternatively, in someembodiments, these one or more features are used in the one or moretime-based prediction models but are viewed as outliers or anomaliessuch that they are given little to no weight for predictions, asdescribed herein with respect the base time series forecasting model(s)510.

In some embodiments, the one or more randomness compensating factorsinclude at least one factor from a group of factors consisting of: alocation of a logistics facility (e.g., a sorting center), a quantity ortype of business (e.g., retailer, service or gas station, restaurant,etc.) within a threshold distance (e.g., 1 mile) of the logisticsfacility, a local or national holiday associated with the logisticsfacility, a sale event associated with the logistics facility (e.g., alarge-scale liquidation sale in the same city as the logisticsfacility), a business recall associated with the logistics facility,weather associated with the logistics facility (e.g., weather in a citywhere logistics facility is located), customer behavior associated withthe logistics facility (e.g., parcel manifest information for parcelsreceived at the logistics facility), a geopolitical event associatedwith the logistics facility, and one or more operational failuresassociated with the logistics facility. An example of an “operationalfailure” may be broken down or inactive conveyor belt assemblies, staffshortages at logistics facilities, one or more machines (e.g., scannersor devices) at logistics facilities that are inactive or not working,logistics vehicles that are not working, and/or the like.

Per block 1009, particular embodiments determine a pattern of one ormore observations associated with the one or more randomnesscompensating factors. For instance, particular embodiments determine,one or more of the corresponding time slices, one or more patterns ofobservations associated with the one or more randomness compensatingfactors. For example, referring back to FIG. 7, particular embodimentsdetermine that for the local event randomness compensating factoroccurring on time slice T1 and time slice T2 there is a subtractiveobservation pattern of nearly a 1000 volume count of parcels. In anotherexample, referring back to FIG. 6, row 606 (and rows 608, 610, 612, and614), particular embodiments determine a pattern of observations fortime slices and associated randomness compensating factors 602-2, 602-3,602-4, 602-5. In some embodiments, block 1009 is performed by therandomness compensating factor pattern generator 509 as describedherein.

In some embodiments, the determining of the one or more patterns ofblock 1009 includes using harmonics synthesis, such as using additivesynthesis or negative synthesis. This is described, for example, withrespect to FIG. 6 where the randomness compensating factors aredescribed as “subtractive” or “additive.” This is also described hereinwith respect to the randomness compensating factor generator 509, whichdescribes harmonics synthesis. As described herein, various embodimentsbrake down seemingly random-looking phenomena (e.g., noisy observationsmade at particular time slices) into basic harmonics-style patterns toreduce aggregate randomness. Instead of having one monolithic model or aformula for forecasting, various embodiments use an aggregate method toimprove the accuracy. Embodiments can break down the aggregate randominfluence into several randomness compensating factors and patterns.Some factors could be additive while some could be subtractive. In someinstances, multiplicative seasonality is addressed by the time-basedpredictive model. However, in some embodiments, the randomnesscompensating factor(s) may be multiplicative also.

Per block 1011, based at least in part on the randomness compensatingfactors and/or the patterns of observation(s), particular embodimentsgenerate a prediction or forecast. For instance, some embodiments modifythe prediction of the one or more time-based prediction models based atleast in part on the one or more randomness compensating factors and theone or more patterns. For example, this is described with respect to theprediction modifier 530 of FIG. 5, where the base-time seriesforecasting model(s) 510 first generates a prediction or forecast, butthat prediction can later be weighted, aggregated, or otherwise changedby the prediction modifier 530 based on the functionality of therandomness compensating factor engine 514. In some embodiments, block1011 is described with respect to the functionality of the outputtime-based prediction 514 of FIG. 5 and/or the prediction modifier 530of FIG. 5.

Alternatively or additionally, block 1011 can include using theprediction of the one or more time-based prediction models to generateanother prediction. Such “another prediction” can include an aggregateprediction that uses the prediction value of the time-based predictionmodel(s) that does not necessarily “modify” the time-based predictionmodel(s) but takes this prediction into account for generating anoverall prediction. For example, embodiments can take the predictionvalue of the one or more time-based prediction models and generate a newvalue based on weighting or otherwise changing the time-based predictionmodel value(s). This is described with respect to the predictionmodifier 530 of FIG. 5. In this way, particular embodiments can predicta set of values (e.g., a second one or more values) for at least one ofthe one or more future time slices based at least in part on thedetermining of the one or more randomness compensating factors and/orthe prediction of the one or more time-based prediction models.

In some embodiments, the modifying of the prediction of the one or morebase time-series forecasting models (or time-based prediction models)includes changing a volume prediction (e.g., predicting a quantity ofparcels that will arrive at a particular logistics facility) for aparticular logistics facility from a first value to a second value for aparticular time slice. This is illustrated, for example, with respect toFIGS. 8A and 8C where the original value on day 3 was forecasted to be34.2 by the base model (FIG. 8A), but embodiments changed the day 3prediction to be 52, as illustrated in FIG. 8C.

Some embodiments start with a standard time series forecasting model atevery logistics facility (or other domain) but also adds one or morerandomness compensating factors, along with a random factor (residualrandomness), to represent residual randomness. The factors or featuresthat affect different logistics facilities (or domains generally) may bevery different. For example, the center that services a large onlineretailer may have very different factors compared to a rural center thatis services a low seasonality of business. Known severe weatherlocations may have a different factor compared to centers that servegovernment agencies which may be affected by geo-political activities. A“domain” as described herein is a knowledge representation, category,topic, or other data set that predictions or forecasts are made for(disregarding predictions for other domains). For example, a firstdomain can be a first logistics facility and a second domain may be asecond logistics facility. In another example, a first domain can be afirst retailer and a second domain can be a second retailer. In yetanother example, a first domain can be a first city and a second domaincan be a second city. For each domain there may be specific randomnesscompensating factors (e.g., weather, sales events, etc.) that affectonly the specific domains

In these instances where different features can affect differentdomains, the one or more randomness compensating factors and themodified prediction are determined for a first domain (e.g., a firstlogistics facility). Additionally, embodiments determine, for a seconddomain (e.g., a second logistics facility), a second set of randomnesscompensating factors different than the one or more randomnesscompensating factors. One or more other patterns of observationsassociated with the second set of randomness compensating factors can bedetermined. Based on the second set of randomness compensating factorsand the one or more other patterns, embodiments change the prediction ofthe one or more time-based prediction models to a value different than avalue made based on the modification of the prediction. For example,referring back to FIGS. 8A and 8C, in addition to embodimentscalculating the forecasts for logistics facility Y, embodiments canadditionally calculate separate forecasts for another logistics facilityZ (e.g., similar to FIG. 8A) using different randomness compensatingfactors B (which are different than the randomness compensating factor Aused in FIG. 8C) such that other patterns are captured and predictionsare made similar to what is illustrated by the table 800-1 of FIG. 8C.

In addition to block 1011, some embodiments additionally determineresidual randomness based at least in part on the modified predictionand the plurality of observations. For example, this is described withrespect to the residual randomness determiner 512 of FIG. 5. Someembodiments alternatively or additionally generate a confidence intervalbased at least in part on a difference between the another generatedprediction and the plurality of observations. For example, this isdescribed with respect to the residual randomness determiner 512 of FIG.5.

Some embodiments generate the prediction at block 1011 as represented inthe following formula:

${F(t)} = {{F_{b}(t)} + {\underset{i = 0}{\sum\limits^{i = n}}{f_{i}(t)}} + {R(t)}}$

where F(t) represents a forecast for a particular domain for the timeframe t, and where F_(b)(t) represents a base time series forecastingmodel prediction for the particular domain, and where f_(i)(t)represents a randomness compensating factor for a domain that is notpart of the base forecast up to n factors for the particular domain, andwhere R(t) represents residual randomness that is not modeled for theparticular domain using a randomness compensating factor. For everybuilding, customer data, seasonality data, operation exceptions,customer provided forecast data, operational data, and/or other data,this formula can use randomness compensating factors for each domain(e.g., and each sort type). The identified randomness compensatingfactors influence and augment the output of the standard base modeltime-series output. In some embodiments, once these factors aredetermined, regression analysis is done to determine residual randomnessand a confidence level is created for the aggregate forecast.Collectively, the base model output with addition of randomnesscompensating factors and residual randomness provide a high degree ofaccuracy to the forecast relative to existing technologies.

Per block 1013, particular embodiments present, to a computing device,an indication associated with the prediction. An “indication” asdescribed herein refers to actual content or payload of the predictionitself (e.g., some or all of the data indicated in FIG. 8C) orinformation associated with the content or payload, such as a warning(e.g., the “WARNING” indicated of FIG. 9) or other information relatedto the prediction or forecast. In an illustrative example of block 1013,in response to the generating/modifying of the prediction (or generatingof the another prediction) of block 1011, embodiments present, to a usercomputer device, an indication associated with the modified prediction,as described with respect to the presentation layer 516. In someembodiments, the presentation layer 516 performs block 1013.

Alternatively or in addition to block 1013, some embodiments cause acontrol signal to be transmitted to a machine, apparatus, or article ofmanufacture for further functionality. Examples of this are describedwith respect to the control signal propagator 518 of FIG. 5. Forexample, some embodiments communicate, via an API, with the controlsignal propagator 518, which causes the control signal propagator 518 tosend a control signal (or computer instruction) to: the logisticsvehicle 120, conveyor belt apparatus, mobile computing entity 110, orother device to cause any functionality as described herein with respectto the control signal propagator 518.

Definitions

“And/or” is the inclusive disjunction, also known as the logicaldisjunction and commonly known as the “inclusive or.” For example, thephrase “A, B, and/or C,” means that at least one of A or B or C is true;and “A, B, and/or C” is only false if each of A and B and C is false.

A “set of” items means there exists one or more items; there must existat least one item, but there can also be two, three, or more items. A“subset of” items means there exists one or more items within a groupingof items that contain a common characteristic.

A “plurality of” items means there exists more than one item; there mustexist at least two items, but there can also be three, four, or moreitems.

“Includes” and any variants (e.g., including, include, etc.) means,unless explicitly noted otherwise, “includes, but is not necessarilylimited to.”

A “user” or a “subscriber” includes, but is not necessarily limited to:(i) a single individual human; (ii) an artificial intelligence entitywith sufficient intelligence to act in the place of a single individualhuman or more than one human; (iii) a business entity for which actionsare being taken by a single individual human or more than one human;and/or (iv) a combination of any one or more related “users” or“subscribers” acting as a single “user” or “subscriber.”

The terms “receive,” “provide,” “send,” “input,” “output,” and “report”should not be taken to indicate or imply, unless otherwise explicitlyspecified: (i) any particular degree of directness with respect to therelationship between an object and a subject; and/or (ii) a presence orabsence of a set of intermediate components, intermediate actions,and/or things interposed between an object and a subject.

A “data store” as described herein is any type of repository for storingand/or managing data, whether the data is structured, unstructured, orsemi-structured. For example, a data store can be or include one ormore: databases, files (e.g., of unstructured data), corpuses, digitaldocuments, etc.

A “module” is any set of hardware, firmware, and/or software thatoperatively works to do a function, without regard to whether the moduleis: (i) in a single local proximity; (ii) distributed over a wide area;(iii) in a single proximity within a larger piece of software code; (iv)located within a single piece of software code; (v) located in a singlestorage device, memory, or medium; (vi) mechanically connected; (vii)electrically connected; and/or (viii) connected in data communication. A“sub-module” is a “module” within a “module.”

The terms first (e.g., first request), second (e.g., second request),etc. are not to be construed as denoting or implying order or timesequences unless expressly indicated otherwise. Rather, they are to beconstrued as distinguishing two or more elements. In some embodiments,the two or more elements, although distinguishable, have the samemakeup. For example, a first memory and a second memory may indeed betwo separate memories but they both may be RAM devices that have thesame storage capacity (e.g., 4 GB).

The term “causing” or “cause” means that one or more systems (e.g.,computing devices) and/or components (e.g., processors) may in inisolation or in combination with other systems and/or components bringabout or help bring about a particular result or effect. For example,the analysis computing entity 105 may “cause” a message to be displayedto a computing entity 110 (e.g., via transmitting a message to the userdevice) and/or the same computing entity 110 may “cause” the samemessage to be displayed (e.g., via a processor that executesinstructions and data in a display memory of the user device).Accordingly, one or both systems may in isolation or together “cause”the effect of displaying a message.

The term “real time” includes any time frame of sufficiently shortduration as to provide reasonable response time for informationprocessing as described. Additionally, the term “real time” includeswhat is commonly termed “near real time,” generally any time frame ofsufficiently short duration as to provide reasonable response time foron-demand information processing as described (e.g., within a portion ofa second or within a few seconds). These terms, while difficult toprecisely define, are well understood by those skilled in the art.

V. Conclusion

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing description and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation, unlessdescribed otherwise.

What is claimed is:
 1. A system comprising: at least one computingdevice having one or more processors; and at least one computer readablestorage medium having program instructions embodied therewith, theprogram instructions readable or executable by the one or moreprocessors to cause the system to: receive input data that includes aplurality of observations made at corresponding time slices; provide theinput data to one or more base time-series forecasting models thatpredict one or more future values for one or more future time slicesbased on the one or more time-series forecasting models analyzing theinput data; determine one or more randomness compensating factors, theone or more randomness compensating factors correspond to one or morefeatures indicative of one or more events that occur on one or more ofthe corresponding time slices, wherein the one or more features are notindicated in the one or more base time-series forecasting models;determine, for one or more of the corresponding time slices, one or morepatterns of observations associated with the one or more randomnesscompensating factors; based at least in part on the one or morerandomness compensating factors and the one or more patterns, modify theprediction of the one or more base time-series forecasting models; andin response to the modifying of the prediction, presenting, to a usercomputer device, an indication associated with the modified prediction.2. The system of claim 1, wherein that at least one processor furthercauses the system to determine residual randomness based at least inpart on the modified prediction and the plurality of observations. 3.The system of claim 1, wherein the determining of the one or morepatterns includes using harmonics synthesis.
 4. The system of claim 1,wherein the modifying of the prediction of the one or more basetime-series forecasting models includes changing a volume prediction fora particular logistics facility from a first value to a second value fora particular time slice.
 5. The system of claim 1, wherein the one ormore randomness compensating factors include at least one factor from agroup of factors consisting of: a location of a logistics facility, aquantity or type of business within a threshold distance of thelogistics facility, a local or national holiday associated with thelogistics facility, a sale event associated with the logistics facility,a business recall associated with the logistics facility, weatherassociated with the logistics facility, customer behavior associatedwith the logistics facility, a geopolitical event associated with thelogistics facility, and one or more operational failures associated withthe logistics facility.
 6. The system of claim 1, wherein the input dataincludes at least one set of data from a group of data consisting of:telematics data captured from a logistics vehicle, sensor data capturedfrom a logistics vehicle, data captured from a parcel manifest, and anydata associated with a parcel.
 7. The system of claim 1, wherein the oneor more randomness compensating factors and the modified prediction aredetermined for a first domain, and wherein that at least one processorfurther causes the system to: determine, for a second domain, a secondset of randomness compensating factors, the second set of randomnesscompensating factors being different than the one or more randomnesscompensating factors, wherein the second set of randomness compensatingfactors are not indicated in the one or more base time-seriesforecasting models; determine, for one or more of the corresponding timeslices, one or more other patterns of observations associated with thesecond set of randomness compensating factors; and based on the secondset of randomness compensating factors and the one or more otherpatterns, change the prediction of the one or more base time-seriesforecasting models to a value different than a value made based on themodification of the prediction.
 8. A computer-implemented methodcomprising: receiving input data that includes a plurality ofobservations made at corresponding time slices; provide the input datato one or more time-based prediction models that predict one or morefuture values for one or more future time slices; determine one or morerandomness compensating factors, the one or more randomness compensatingfactors correspond to one or more features indicative of one or moreevents that occur on one or more of the corresponding time slices;determine, for one or more of the corresponding time slices, one or morepatterns of observations associated with the one or more randomnesscompensating factors; based at least in part on the one or morerandomness compensating factors and the one or more patterns, modify theprediction of the one or more time-based prediction models; and inresponse to the modifying of the prediction, presenting, to a usercomputer device, an indication associated with the modified prediction.9. The method of claim 8, further comprising, in response to themodifying of the prediction, generating a confidence interval basedleast in part on a difference between the modified prediction and theplurality of observations.
 10. The method of claim 8, wherein thedetermining of the one or more patterns includes using additivesynthesis or negative synthesis.
 11. The method of claim 8, wherein thepredicting by the one or more time-based prediction models includespredicting a quantity of parcels that will arrive at a logisticsfacility.
 12. The method of claim 8, wherein the one or more randomnesscompensating factors include: a location of a logistics facility,weather associated with the logistics facility, and customer behaviorassociated with the logistics facility.
 13. The method of claim 8,wherein the input data includes data captured from a parcel manifest,and any data associated with a parcel.
 14. The method of claim 8,wherein the one or more randomness compensating factors and the modifiedprediction are determined for a first logistics facility, and whereinthat at least one processor further causes the system to: determine, fora second logistics facility, a second set of randomness compensatingfactors, the second set of randomness compensating factors beingdifferent than the one or more randomness compensating factors, whereinthe second set of randomness compensating factors are not indicated inthe one or more time-based prediction models; determine, for one or moreof the corresponding time slices, one or more other patterns ofobservations associated with the second set of randomness compensatingfactors; and based on the second set of randomness compensating factorsand the one or more other patterns, change the prediction of the one ormore time-based prediction models to a value different than a value madebased on the modification of the prediction.
 15. A computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by one or more processors to cause theone or more processors to: receive input data that includes one or moreobservations made at one or more corresponding time slices; provide theinput data to one or more time-based prediction models that predict oneor more values for one or more future time slices; determine one or morerandomness compensating factors, the one or more randomness compensatingfactors correspond to one or more features indicative of one or moreevents associated with the one or more corresponding time slices; basedat least in part on the determining of the one or more randomnesscompensating factors and the prediction of the one or more time-basedprediction models, predict a second one or more values for at least oneof the one or more future time slices; and in response to the predictingof the second one or more values, presenting, to a user computer device,an indication associated with the predicted second one or more values.16. The computer readable storage medium of claim 15, wherein the one ormore processors are further caused to determine residual randomnessbased at least in part on the prediction of the second one or morevalues and the one or more observations.
 17. The computer readablestorage medium of claim 15, wherein the one or more processors arefurther caused to determine one or more patterns associated with the oneor more randomness compensating factors.
 18. The computer readablestorage medium of claim 15, wherein the prediction of the second one ormore values includes changing a volume prediction for a particularlogistics facility from a first value predicted by the time-basedprediction model to a second value for a particular time slice.
 19. Thecomputer readable storage medium of claim 15, wherein the one or morerandomness compensating factors include at least one factor from a groupof factors consisting of: a location of a logistics facility, a quantityor type of business within a threshold distance of the logisticsfacility, a local or national holiday associated with the logisticsfacility, a sale event associated with the logistics facility, abusiness recall associated with the logistics facility, weatherassociated with the logistics facility, customer behavior associatedwith the logistics facility, a geopolitical event associated with thelogistics facility, and one or more operational failures associated withthe logistics facility.
 20. The computer readable storage medium ofclaim 15, wherein the input data includes at least one set of data froma group of data consisting of: telematics data captured from a logisticsvehicle, sensor data captured from a logistics vehicle, data capturedfrom a parcel manifest, and any data associated with a parcel.